2019 ASIA PACIFIC CONFERENCE ON (APCoRISE) · 2019. 4. 13. · KEYNOTE SPEAKER 1 Planning for the...

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2019 ASIA PACIFIC CONFERENCE ON

RESEARCH IN INDUSTRIAL AND

SYSTEMS ENGINEERING

(APCoRISE)

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Message from Head of Department The challenge of constantly changing market, will lead to the revolution of industrial production, especially

to the new product development process. This so called industry 4.0 technology is concerned with direct

and smart production, where the design was created with customer integration by the 3D printer which is

connected to the production location. The individualized product seamlessly enters the industrial

development process of the end product, hereby customer request can be met, market changes can be

responded to, and the wide supply spectrum can be generated for the same product. Furthermore, the

amount of resources and energy use will be much lower without compromising product quality.

Department of Industrial Engineering, Universitas Indonesia has a responsibility to encourage academician

and engineers, particularly in this region to actively involved in developing innovative solutions in the era

of Industry 4.0. 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering

(APCoRISE) is a part of our initiative to provide a forum for researchers, engineers, and professionals to

discuss and exchange the current research, the new technology and solutions in industrial and systems

engineering. This conference is expected to foster the development of innovative solutions by integrating

the role of people, process and technology.

Last but not least, I would like to express my sincere gratitude and appreciation to our keynote speakers,

international advisory board and all our organizing and technical committees who have been providing

outstanding commitment and support to make this conference a success in the first place. I also thank all

the conference participants for attending APCoRISE 2019 and wish you a pleasant stay in Jakarta.

Sincerely,

Dr. -Ing Amalia Suzianti, ST, MSc

Head of Industrial Engineering Departement

Faculty of Engineering, Universitas Indonesia

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Message From General Chair

It is my great pleasure to welcome you to 2019 Asia Pacific Conference on Research in Industrial and

Systems Engineering (APCoRISE) takes place in Depok, Indonesia on April 18-19 April, 2019. It has been

a real honor and privilege to serve as the General Chair of the conference. Extensive research and

development in the past few years has pushed industrial and systems engineering field into state-of-the-

art application areas such as intelligent transportation system, human-computer interaction, advanced

manufacturing system. However, many critical issues on building reliable, robust, and human-focused

systems are still open for research.

APCoRISE 2019 has received 143 paper submissions from 5 countries. After a rigorous review process,

the program committee accepted 65 high quality full papers for inclusion in the conference proceedings.

Thus, the conference is hoped to became a great event for scholars and practitioners in industrial and

systems engineering research area, to present, disseminate, and discuss their research results. APCoRISE

is a part of our initiative to provide a forum for researchers, engineers, and professionals to discuss and

exchange the current research, the new technology and solutions in industrial engineering area.

APCoRISE 2019 is honored to have two distinguished keynote speakers, Prof. Igor Mayer from NHTV

Breda University of Applied Sciences, the Netherlands and Prof. Nyoman Pujawan from Sepuluh

Nopember Institute of Technology. Igor Mayer (1965) is a professor (lector) of Applied Games, Innovation

& Society at Breda University of Applied Sciences. He is also the founder and, owner of Signature Games

(www.signaturegames.eu). From 1998 until January 2015, he was a senior associate professor in the faculty

of Technology, Policy and Management (TPM) in Delft University of Technology, the Netherlands, where

he set up and led the policy gaming research group. On the other hand, Prof. Nyoman is a well-known

expert in Supply chain management in Indonesia. Currently, he is the editor in chief, Operations and Supply

Chain Management: An International Journal.

I would like to close this welcome with a round of thanks for more than 40 program committee members

and conference organizers who have made APCoRISE 2019 possible. I would like to start by thanking my

fellow members of the Technical Program Committee who took on many of the responsibilities associated

with paper submission and the selection of the venues and the budget; Scientific Committee for their hard

work in managing the reviewing process; and the many individual reviewers for helping us select the papers

to be presented. I would like to thank our invited speakers for agreeing to take time out of their busy

schedules to give us their perspectives on a broad-ranging set of topics. I would particularly like to extend

our gratitude to our sponsors, the Department of Industrial Engineering Universitas Indonesia and IEEE

Indonesia Section for supporting the conference and for making this the conference with the highest level

of sponsorship to date.

Once again, welcome to Depok.

Dr. Komarudin, ST, MEng

General Chair, APCoRISE 2019, Depok, Indonesia

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KEYNOTE SPEAKER 1

Planning for the Future with Games and Virtual Reality: the Maritime

Spatial Planning Challenge

Prof. Igor Mayer - NHTV Breda University of Applied Sciences, the Netherlands

Abstract:

In the seminal book ‘Gaming: the future’s language’, Duke [1] argues that a simulation

game or serious game, is an excellent communication and learning tool for planning and

decision-making. Through play, planners and stakeholders experientially understand the dynamic interrelations

among various subsystems, the interdependencies among the actors and the consequences of actions well into the

future. Simulation games or serious games have thus become connected to a communicative and learning style of

planning. This implies that Planning Support Systems (PSS) should gear to facilitate evidence-based, social

interaction among stakeholders, planners and experts. A number of innovative PSS in terrestrial planning have found

ways to incorporate scientific models and data into multi-player, digital game platforms with an element of playful

interaction. However, when it comes to the globally important issue of spatial planning ‘at sea’ – called Maritime

Spatial Planning (MSP) – systems are still early in their innovation curve, and the use and usefulness of existing

approaches and tools still needs to be demonstrated in use cases. The 2014 EU Directive on Maritime Spatial Planning

(MSP) for instance lays down obligations for the EU Member States to establish a maritime planning process,

resulting in a maritime spatial plan by 2020. To facilitate evidence-based stakeholder engagement in MSP, the ‘MSP

Challenge’ games – a board game [2] and a simulation platform - were designed and played as part of many

stakeholder events in different European countries [2,3]. The Maritime Spatial Planning Challenge board game is

multi-player, table top strategy game played on a 2.8 × 1.6 map of a fictional but realistic sea basin shared by three

countries. It has proved to be a very effective 1-2 hours introduction for planners (to be) and stakeholders to

understand the complexity of Maritime Spatial Planning. The MSP simulation platform is an innovative example of

an integrated, interactive planning support system for MSP. It integrates real geo- and marine data provided by a

great many proprietary institutions with Blue Growth simulation models for shipping, energy and ecology (Ecopath

with Ecosim), linked together in a Unity game engine-based interactive platform. This set up allows anyone – experts

as well as non-experts - to playfully operate it in multi-player, game sessions, for purposes such as education,

stakeholder engagement, scenario exploration and co-design. The current MSP simulation platform hosts a bespoke

edition created for the Clyde Marine area in Scotland and the complete Baltic and North Sea basins. The platform is

built in highly modular fashion so that it can host any sea basin in the world.

Further reading

1. Duke, R.D. Gaming: The future’s language; 1st ed.; Sage Publications: New York, 1974; ISBN 0-470-22405-3. 2. Keijser, X.; Ripken, M.; Mayer, I.; Warmelink, H.; Abspoel, L.; Fairgrieve, R.; Paris, C. Stakeholder Engagement in Maritime Spatial Planning: The

Efficacy of a Serious Game Approach. Water 2018, 10, 724, doi:10.3390/w10060724.

3. Abspoel, L.; Mayer, I.; Keijser, X.; Warmelink, H.; Fairgrieve, R.; Ripken, M.; Abramic, A.; Kannen, A.; Cormier, R.; Kidd, S. Communicating Maritime Spatial Planning: The MSP Challenge approach. Mar. Policy 2019, doi:10.1016/j.marpol.2019.02.057.

Short Biography:

Prof. Igor Mayer (1965) is a professor (lector) of Applied Games, Innovation & Society at Breda University of

Applied Sciences, the Netherlands. Early 2017, he was awarded a Hai Tian (Sea Sky) scholarship for three years in

Dalian University of Technology (DUT), Dalian, China. Previously (2015-‘16), he has been a visiting professor in

the School of Management and Economics of Beijing Institute of Technology (BIT), China. From 1998 until January

2015, he was a senior associate professor in the faculty of Technology, Policy and Management (TPM) in Delft

University of Technology, the Netherlands, where he set up and led the policy gaming research group. His pending

research line has the title “Playful Organizations & Learning Systems”. Over the years and in various partnerships,

he has initiated, managed and participated in a large number of serious gaming-related research and development

projects. He has been a partner in several European projects, part of FP7, H2020, Eranet, Interreg and Erasmus

programs. One featured project is the MSP Challenge 2050 (www.mspchallenge.info) with pending EU / Interreg

funded projects in the North Sea, Baltic and Celtic regions, and invited game-play sessions around the globe. He has

published more than 135 journal and conference papers (H > 26, see. www.researchgate.net/profile/Igor_Mayer). He

has been the promotor of six completed PhD theses, with three ‘award winning’.

http://www.mspchallenge.info/

http://www.researchgate.net/profile/Igor_Mayer

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KEYNOTE SPEAKER 2

Managing Uncertainty in the Supply Chain

Prof. Ir. I Nyoman Pujawan, MEng., PhD

Institut Teknologi Sepuluh Nopember (ITS), Indonesia

Abstract:

Supply chain is inherently complex and uncertain due to a number of reasons.

First is due to globalization where products are travelling for a longer distance

and involving multiple countries. Second, it is the result of outsourcing trend

where more parties are now involved. Third is due to innovation that triggers the increase in product variety

and shortening of product life cycle. And fourth is recent technology innovation that disrupts old business

models and create new landscape for competition. This presentation will outline the uncertainty presents

in the supply chain and various strategies that companies can opt for managing supply chain uncertainty.

The strategies are classified into reactive and proactive. Reactive strategies are basically buffering

strategies where uncertainty is responded by adding safety buffers in the form of inventory, time buffer,

having extra capacity, etc. The proactive strategies include redesigning the supply chain, collaborating with

partners, and transforming to new business models.

Short Biography:

Prof. Ir. I Nyoman Pujawan, M.Eng., Ph.D is Professor of Supply Chain Engineering at the Department of

Industrial Engineering and the Head of the Technology Management Department, Institut Teknologi

Sepuluh Nopember (ITS), Surabaya, Indonesia. He is currently the President of the Indonesian Supply

Chain and Logistics Institute (ISLI). He received a Bachelor degree in Industrial Engineering from ITS,

Indonesia, Master of Engineering in Industrial Engineering from Asian Institute of Technology (AIT)

Bangkok, Thailand, and PhD in Management Science from Lancaster University, UK. He also holds

Certified Supply Chain Professional (CSCP) from APICS (USA). He was a Lecturer in Operations

Management in Manchester Business School, The University of Manchester, UK in 2003 – 2004. His

papers have appeared in many international journals including the European Journal of Operational

Research, International Journal of Production Economics, International Journal of Production Research,

Production Planning and Control, International Journal of Physical Distribution and Logistics

Management, Business Process Management Journal, among others. He is the Editor-in-Chief of

Operations and Supply Chain Management: An International Journal and in the Editorial Board of few

other international journals. He is a Board Executive Member of the Asia Pacific Industrial Engineering

and Management Systems Society (APIEMS) and the International Federation of Logistics and SCM

Systems (IFLS). Professor Pujawan worked in industry before moving to the academia. While his academic

background is very strong, he is equally well experienced in handling industry problems. He is an active

consultant for various supply chain and logistics related industry problems (has involved in over 40

consulting projects). He is a frequent invited speakers for both academic as well as industry forum,

nationally as well as internationally. He is now a member of committee responsible for designing national

logistics systems under the Coordinating Ministry for the Economy, Republic of Indonesia. In the last few

years, he is also teaching in few universities abroad including Mahidol University, Mae Fah Luang

University, and King Mongkut’s University of Technology North Bangkok.

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CONFERENCE COMMITTEE

GENERAL CHAIR

Dr. Komarudin, ST, MEng, Universitas Indonesia

INTERNATIONAL ADVISORY BOARD

Prof. Ir. Isti Surjandari Prajitno, MT, MA, PhD, Universitas Indonesia, Indonesia

Prof. Dr. Ir. Teuku Yuri M. Zagloel, M.EngSc, Universitas Indonesia, Indonesia

Prof. Dr. Ir. Fitri Yuli Zulkifli, ST, MSc, Universitas Indonesia, Indonesia

Prof. Ir. Budi Santosa, MS, PhD, Institut Teknologi Sepuluh Nopember

Prof. Dr. Pekka Leviäkangas, VTT Technical Research Centre of Finland Ltd. & University of Oulu, Finland

Dr. Wahyudi Sutopo, ST, Msi, Universitas Sebelas Maret

Dr. Lamto Widodo, ST, MT, Universitas Tarumanegara

STEERING COMMITTEE

Dr. -Ing Amalia Suzianti, ST, MSc, Universitas Indonesia

Dr. Akhmad Hidayanto, ST, MBT, Universitas Indonesia

Ir. Erlinda Muslim, MEE, Universitas Indonesia

Armand Omar Moeis, ST, MSc, Universitas Indonesia

SCIENTIFIC COMMITTEE

Dr. Andri D. Setiawan, ST, MSc, Universitas Indonesia

Dr. rer.pol. Romadhani Ardi, ST, MT, Universitas Indonesia

Dr. Zulkarnain, ST, MT, Universitas Indonesia

Arian Dhini, ST, MT, Universitas Indonesia

Dr. -Ing Asep Ridwan, ST, MT, Universitas Sultan Ageng Tirtayasa

Budhi Sholeh Wibowo, ST, MT, MBA, PDEng, Universitas Gadjah Mada

Putu Dana Karningsih, ST, MEngSc, PhD, Institut Teknologi Sepuluh November

Armin Darmawan, ST, MT, Universitas Hasanudin

TECHNICAL PROGRAM COMMITTEE

Chair: Dr. Komarudin, ST, MEng, Universitas Indonesia

Member:

Dr. Zulkarnain, ST, MT, Universitas Indonesia

Dr.rer.pol Romadhani Ardi, ST, MT, Universitas Indonesia

Dr. Andri D. Setiawan, ST, MSc, Universitas Indonesia

Arian Dhini, ST, MT, Universitas Indonesia

Annisa Marlin Masbar Rus, ST, MSc, Universitas Indonesia

Irvanu Rahman, ST, MPA, Universitas Indonesia

Billy Muhammad Iqbal, S.Ds, MT, Universitas Indonesia

Danu Hadi Syaifullah, ST, MScSF, Universitas Indonesia

Arry Rahmawan, ST, MT, Universitas Indonesia

Inaki Maulida Hakim, ST, MT, Universitas Indonesia

Maya Arlini Puspasari, ST, MT, Universitas Indonesia

Enrico Laoh, ST, MT, Universitas Indonesia

Andri Mubarak, ST, MSc, Universitas Indonesia

https://www.apcorise.com/pekka-leviakangas/

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CONFERENCE SCHEDULE

Time Event

18 April 2019

07.30-09.00 Registration

09.00-09.15 Opening Ceremony

09.15-09.20 Opening Speech from General Chair of The 2nd Asia Pacific Conference on

Research in Industrial and Systems Engineering (APCoRISE) 2019

Dr. Komarudin, MEng

09.20-09.25 Opening Speech from Head of Department of Industrial Engineering,

Universitas

Indonesia

Dr.-Ing. Amalia Suzianti, ST, MSc

09.25-09.30 Opening Speech from Dean of Faculty of Engineering, Universitas

Indonesia

Dr. Ir. Hendri D.S. Budiono, MEng

09.30-09.35 Photo Session

09.35-10.00 Coffee Break

10.00-10.15 Introduction to IEEE

Prof. Dr. Ir. Fitri Yuli Zulkifli, ST, MSc (Former President of IEEE

Indonesia Section)

10.15-11.10 Keynote Speaker 1: Planning for the Future with Games and Virtual

Reality: the Maritime Spatial Planning Challenge

Prof. Igor Mayer

11.10-12.00 Keynote Speaker 2: Managing Uncertainty in the Supply Chain

Prof. Ir. I Nyoman Pujawan, MEng, PhD

12.00-13.00 Lunch Break

13.00-15.00 Parallel Session 1

15.00-15.30 Coffee Break


17.00-19.00 Free Time

19.00-20.00 Best Paper Announcement and Dinner

19 April 2019


10.00-12.00 Closing

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Session 1- Room A (13.00-15.00)

Presentation

Order Paper ID Title Presenter Page

1 1570513207 Price Estimation Model Using Factor

Analysis in Procurement

Achmad Faizal 12

2 1570513224 Sentiment analysis of standardization using

deep belief network: a case of Indonesian

National Standards

Aries Agus Budi

Hartanto

13

3 1570513225 Scalable Data Analytics from

Predevelopment to Large Scale

Manufacturing

Ulrich Tobis

Bührer

14

4 1570520859 Crimes Prediction Using Spatio-Temporal

Data and Kernel Density Estimation

Vinnia Kemala

Putri

38

5 1570513237 Analysis of Driver Acceptance Level

Towards Advanced Driver Assistance

Systems in Jakarta

Ahmad Zaki 16

6 1570513243 An Improved Accident Analysis Model for

The Scheduled Civil Aviation Industry in

Indonesia

Rhahadian Bima

Saputra

17

7 1570521277 On the Performance Similarity Between

Exponential Moving Average and Discrete

Linear Kalman Filter

Muhammad

Fikri

41

8 1570524192 Protection System Failure on 150kV

Transmission Line in Java-Bali Grid due to

Fault Current Residual

Aristo Adi

Kusuma

49

Session 1- Room B (13.00-15.00)

Presentation


1 1570513274 Conceptual Modeling of Safety Culture in

Coal Steam Power Plant Operations and

Maintenance Services in Indonesia

Edwin

Hermawan

21

2 1570513361 Development of Resilience Management

Cockpit Framework to Startup Enterprise in

Indonesia

Dimas Prabu

Tejonugroho

32

3 1570523622 Techno-Economic Analysis of Narrowband

IoT (NB-IoT) Deployment for Smart

Metering

Amriane

Hidayati

43

4 1570524071 Multicriteria Decision Approach for

Selection of Fault Current Limiters

Technology

Handrea

Bernando

Tambunan

46

5 1570524152 Six Sigma for Evaluating Electronic

Signature in eProcurement System: A Case

Study

Antoni Wibowo 48

6 1570524216 Techno Economics Study of Spectrum

Sharing for Mobile Network Operator in

Rural Area

Lia Hafiza 59

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Session 1- Room C (13.00-15.00)

Presentation


1 1570524206 Data Warehouse Development for Credit

System

Tiffany Tantri 50

2 1570512429 An Image Processing and Artificial

Intelligence based Traffic Signal Control

System of DHAKA

Abu Salman

Shaikat

4

3 1570513153 An Improved Pupil Detection Method

under Eyeglass Occlusions

Sabrina 9

4 1570520886 Lower Back Pain Classification Using

Machine Learning

Akhmad Dyma

Habib Syababa

39

5 1570520991 Preliminary Study on Machine Learning

Application for Parkinson's Disease

Diagnosis

Jessika 40

6 1570522315 Pattern Recognition using Machine

Learning for Cancer Classification

Marvel Sugi

Hartono

53

7 1570513336 Designing Organizational Persona in

Understanding B2B Environment Using

Cluster Analysis

Arsila

Chairunnisa

28

8 1570523734 A Classification of Research on New

Product Development in Small Medium

Enterprises

Muhammad

Iqbal

55

Session 2- Room A (15.30-17.00)

Presentation


1 1570513317 A Review of Response Surface

Methodology Approach in Supply Chain

Management

Januardi 25

2 1570513373 A Conceptual Framework of Reverse

Supply Chain Activities in Process

Industries

Muhammad

Fadhlun Adzim

34

3 1570513233 Designing Theoretical Framework for

Measuring Burnout related to Academic

System Amongst University Student

Julya Ade Jhora 15

4 1570510589 Development of PLC and SCADA based

Spray Coating System for Application in

Glass Bottle Manufacturing Industries of

Bangladesh

Abu Salman

Shaikat

1

5 1570513130 Understanding the Dynamics of Using

Tobacco Excise as an Earmarked Fund for

Financing Universal Health Coverage in

Indonesia

Teuku Naraski

Zahari

7

6 1570516814 IoT Learning for Electrical Engineering Virginia Lalujan 37

7 1570524174 Measurement of Web-Based Merchant

Application Portal (MAP) Using Function

Irvan Santoso 57

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Presentation


Point Analysis and Constructive Cost

Model II

8 1570513366 Adoption of Halal Supply Chain in

Indonesia: A Preliminary Insight

Siti Khodijah 33

Session 2- Room B (15.30-17.00)

Presentation


1 1570513268 A Causal Modelling Proposal for WEEE

Management System Funding Scheme in

Indonesia

Asy'ari Fauzan 20

2 1570513125 Conceptual Model of Household

Consumer Behavior in Storing WEEE

(Waste Electrical and Electronic

Equipment)

Robby Marlon

Brando

5

3 1570513392 Initial Design of Electronic Waste

Management Model in Indonesia Based

on The Extended Producer Responsibility

Concept From Regulator Perspective

Bernardo

Mariano

35

4 1570513187 Model Conceptualization for Optimal

Strategies in Transboundary Movement of

Waste Electrical and Electronic

Equipment: A Game Theory Approach

Pilamupih Dwi

Rahayu

11

5 1570513261 Serious Simulation Game Preliminary

Design As Education Tool for Waste

Electrical and Electronic Equipment

Management System in Indonesia

Laksmi

Ambarwati

18

6 1570513276 Conceptual Model of Understanding

Procurement Fraud in Indonesia

Wahyu Seto

Syahputra

22

7 1570513136 Exploring the Policy Structure of Aircraft

Industry Development in Indonesia: A

Conceptual Model

Isnaeni Yuli

Arini

8

8 1570513129 Understanding the Structure of Policies in

the Future Implementation of Internet-

Protocol-based Interconnection in

Indonesia

Irma Handayani 6

9 1570523452 The Design of Model and Inventory

Routing Problem (IRP) Algorithm for

Swapped Battery at Battery Exchange

Station (BES): Case Study of Electric

Motors

Nofan Hadi

Ahmad

58

10 1570524307 Designing Conceptual Model of An

Inbound Logistics Consolidation with

Multi-Vendors Single-Buyer in The

International Supply Chain Network

Zulnio

Tarakanantyo

Yudha Perwira

54

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Session 2- Room C (15.30-17.00)

Presentation


1 1570513177 Total Quality Management

Implementation in Small Business: Case

Study in Depok, Indonesia

B. Handoko

Purwojatmiko

10

2 1570513263 Service Quality Assessment of X And Y

Generation Frontliner Using Integration of

Servqual And Kano Model

Asiyah Nur

Mahmudah

19

3 1570527385 Improving Overall Equipment

Effectiveness (OEE) through System

Dynamics and the Internet of Things (IoT)

Yunizar Zen 44

4 1570524217 Power System Inertia Estimation Based

on Frequency Measurement

Joko Hartono 52

5 1570524038 Implementation of ISO 9001 in Indonesia

Automotive Component Manufacturing

Industry

Zulfadlillah 45

6 1570524147 Performance Measurement System

Development Using SCOR-Balanced

Scorecard Integrated Model for SME in

Indonesia: A Case Study for MTO

Products in Textile Industry

Huria Nusantara 47

7 1570513354 Inventory Strategy Planning Model with

Fuzzy Analytic Hierarchy Process and

Neural Network Approaches in the

Wiring Industry

Fauzie Rachman 31

8 1570513310 Integration model of spare parts inventory

and preventive maintenance considering

cooling down and machine dismantling

time factors

Fachransjah

Aliunir

24

9 1570513325 A Stock Level Spare Parts by

Classification using ANP - Multi

Attribute Spare Tree Analysis: A Case

Study in Plastic Injection Industry

Oksa Angger

Dumas

26

Session 3- Room A (09.00-10.00)

Presentation


1 1570527365 Internet Of Things-Based Processes

Improvement Of Indonesian Hospital

Egi Aulia

Mahendra

42

2 1570513283 Human Performance Evaluation

Framework in the Complex Control Room

Ghassani

Shabrina

23

3 1570513330 Medical Trainee Scheduling Model

Considering Ergonomic Factors in

Educational Hospital

Tri Novita Sari 27

4 1570513338 Job Rotation Model Considering

Ergonomic Factors in Educational

Institutions

Mirsha Ulfatul

Haqni

29

xii

Session 3- Room B (09.00-10.00)

Presentation


1 1570511506 Social Network Analysis of the Pilkada

Serentak 2018: Towards National

Coalition in the 2019 Indonesia's General

Election

Armand Omar

Moeis

2

2 1570511707 Classifying Twitter Spammer based on

User's Behavior using Decision Tree

Yuli Fitriani 3

3 1570529249 Social and economic aspects when

allocating a 3.5 GHz frequency band for

5G Mobile in Indonesia

Luthfijamil

Setiawan

Sastrawidjaja

60

4 1570527393 Improvement Priority Analysis of

Indonesian Tourism Special Economic

Zone

Eki Ludfiyanti 51

Session 3- Room C (09.00-10.00)

Presentation


1 1570513353 Social Cognitive Modeling On The

Instagram Towards Health Information

Jesilia Saraswati

Putri

30

2 1570515016 Evaluating the Use of a Posterior Load

Carriage Aid in Grass-Carrying Activities

for Cow Farming Industry

Ni Luh Putu

Lilis Sinta

Setiawati

36

3 1570523968 An Environmental Ergonomics Review of

Small Medium Enterprises Workplace

Condition in Indonesia

Dene Herwanto 56

The 2nd Asia-Pacific Conference on Research in Industrial and System Engineering 2019

1

Development of PLC and SCADA based Spray

Coating System for Application in Glass Bottle

Manufacturing Industries of Bangladesh

Abu Salman Shaikat

Dept. of Mechatronics Engineering

World University of Bangladesh

Dhaka, Bangladesh

[email protected]

Hasan Imam



Dhaka, Bangladesh

[email protected]

Rumana Tasnim



Dhaka, Bangladesh

[email protected]

Shanzid Ahmad Rocky



Dhaka, Bangladesh

[email protected]

Mehbub Khan



Dhaka, Bangladesh

[email protected]

Mahbub Alam


World University of Bangladesh Dhaka,

Bangladesh

[email protected]

Abstract—PLCs are robust industrial electronic systems

applied for controlling a wide variety of mechanical systems.

One of the most substantial applications of PLC is found in

glass bottle manufacturing industry. PLC based glass bottle

spray coating system is a noteworthy part of glass bottle

manufacturing industry Especially, in Bangladesh the system

has not yet been introduced till now. This paper aims to

implement a glass bottle spray coating system using

programmable logic controller (PLC) along with SCADA

(WinCC RT advanced) and proportional Integral Derivative

(PID) control system. For glass bottle spray coating, accurate

temperature and density level needs to be maintained, which is

emphasized in our proposed system. In this work, temperature

sensor is used to detect the temperature of water and level

switch is used to detect the water level. WinCC RT advanced is

used for obtaining the real time value.

Keywords—PLC, WinCC RT Advanced, Temperature level,

Density level, Thermocouple, Level switch

I. INTRODUCTION

Spray coating is a process where the varieties of fluids are used in a spray nozzle to create a thin layer. Cold end glass coating can be addressed as a thin film layer used while manufacturing glass bottles and jars. Such glass bottles usually are coated with two surface coatings. One of them being hot end coating applied prior to the annealing process whereas the other one being cold end coating used right after annealing. Increasing the precision of level measurement has a noteworthy impact on reducing the variability in chemical processes, which, in turn, improves product quality as well as decreases costs and wastes. Besides, due to high energy processes involved in manufacturing glass bottles, float glass, technical glass, and fiber glass variants, temperature measurement needs to be precise. At various production stages during manufacturing and process control, an accurate measurement of temperature helps to keep homogeneous quality and to lessen a scrap of the glass products.

The key focus of the spray coating is to keep the proper temperature and density of the fluid. However, to get accurate density is an exigent task. We can derive this from the below equation:

P=ƿgh (1)

Where, P=pressure, ƿ=density, g= gravity, h=height/level

So, for obtaining an accurate density of the fluid, it needs to put an accurate amount of pressure with exact height.

Production of bottle requires lubricated glass coating i. e Oleic Acid Vapour spray coating for general use. This Spray coating provides excellent adhesion, good resistance capability to heat and abrasion, low toxicity for the glass bottle. These characteristics make the coating crucial for manufacturing glass bottles. However, in Bangladesh, presently no industries are using spray coating procedure due to its high cost. This paper aims to develop a prototype system to facilitate spray coating and control as well as maintain an exact temperature and density of fluid using PLC and SCADA. Moreover, to fulfill the requirements of advanced control of accurate density and temperature, glass industries have been using PID control engineering methods (Proportional, Integral, and Derivative controller) since the last few decades. In this work, PLC along with PID control method is used for controlling the overall operation and SCADA (WinCC RT Advanced) is used for monitoring the whole process and graphical representation of temperature changes.

II. RELATED WORKS

Researchers and engineers developed PLC and SCADA based level and temperature controlling system for the application in different industries over the past few decades. Reza Ezuan Samin et. al (2011) implied PID controlling mechanism using Programmable Logic Controller (PLC) in small automation plant by heating the tank. The researchers were able to manage the time for heating up an exact solution to a normal temperature maintaining the system stability. Some performance parameters namely settling time, rise time and percentages overshoot of the controller are observed followed by detailed analysis of the system performance [1]. Bhupesh Aneja et.al (2011) conducted a qualitative review on the application of PLC, microcontrollers and sensors in controlling and maintain accurate temperature [2]. Rishabh Das et.al (2013) detailed an analytical study on the


2

Social Network Analysis of the Pilkada Serentak

2018: Towards National Coalition in the 2019

Indonesia’s General Election

Armand Omar Moeis

Universitas Indonesia

Jakarta,Indonesia

[email protected]

Aziiz Sutrisno

Technische Universiteit Eindhoven

Eindhoven, the Netherlands

[email protected]

Abstract— The Indonesian general election in 2019 marks a

new era for the country democratic evolution. This is the first

time the Presidential election is being held at the same time as

the legislative election. This creates new dynamics on how the

political parties as the country legal proposer of the

presidential candidates. Based on the current law, the legal

threshold for the Indonesian presidential candidates is at least

being proposed by party or coalition of parties with at least

20% of total popular votes in the previous general election.

Parties are searching for optimum configuration to not only to

improve their coalitional presidential candidate winning

probability but also to enhance electability in the legislative

election as coattail effect.

Keywords— Social Network Analytics, Latent Cluster,

Coalition Forming

I. INTRODUCTION

Given the circumstance of the 2019 general election, the

Regional level election in 2018 (Pilkada Serentak) carried

strategic importance in the national level. At least there are

two reasons for this argument. Firstly, the Pilkada Serentak

will be the only testbed for political parties to established

coalitional present in just before the general election. The

Indonesian parties’ coalitions are not built upon ideological

proximity [1], it was formed in a more pragmatic calculation.

That said, the Pilkada Serentak involve the largest block of

eligible voters in many of the most important regions based

on electoral math. Therefore, one might argue that the

collective coalitional winner of the Pilkada Serentak have a

real opportunity to win the general election in 2019

especially in the presidential. Secondly, the Pilkada Serentak

is also an issue testbed. Considering the time vicinity

between Pilkada Serentak 2018 and General election 2019,

issues arises in the former will be more nuanced toward the

later. Consequently, such narratives will shape the beginning

of the campaign year in the general election. This paper aims

to investigate the plausible national coalition of political

parties as a result of collective smaller regional elections in

the Pilkada Serentak. With that being said, we are interested

to answer the question on how the national coalition will

looks like if the regional coalitions are the representative of

the national level policies for all political parties, using

Network Analytics.

II. METHOD

We used latent cluster analysis in the social

network analysis as our method to investigate the possible

hidden coalitional formation of political parties. We will use

the 2018 registered voter data available in the Indonesian

Election Commission website. We then normalized number

of voters in corresponding regions as our weighting factor of

the coalitional formation. That means any coalitions formed

in a more populated area is more valuable than in the fewer.

Furthermore, we created two ranks of weighting factor

based on the Provincial level and the lower regional level.

Furthermore, we use the Infomap algorithm coined by

Rosval and Bergstorm [2] as our means to detect latent

cluster within the collective small networks in the regional

elections. In considerations of the collective network

structure, it is also important to see which parties have

relatively strong positions as bridges between two

coalitions. Therefore, we use eigenvector centrality to

measure the bridging centrality of parties in the network

structure.

III. LITERATURE REVIEW

Social Network Analysis (SNA) is an approach

focuses on the structure of ties within a set of social actors,

e.g., persons, organization, and so on [3]. Borgatti [3]

further coined the importance of SNA in the social sciences

field since it is a compelling approach to analyze social

cohesion, brokerage and exchange, as well as coalitional

formation. Furthermore, the integration of qualitative and

quantitative efforts on SNA has proven to shed different

takes on political narratives.

IV. FINDINGS

Most of the political parties in that participated in

the 2014 general elections took part in the Pilkada Serentak

2018. Golkar, PDIP and Gerindra are parties with the

highest number of candidates across all the regions.

We compared the relative proximities of two

political parties namely PDIP and Gerindra. The following

distinction based on the 2014 Presidential election. In both

results PAN and PPP holds relatively strong relations with

both PDIP and Gerindra which indicates that the coalitional

formation in the regional level are not driven solely by

ideological differences otherwise we will see completely

opposite sets of proximity measurement.


3

Classifying Twitter Spammer based on User’s

Behavior using Decision Tree

Yuli Fitriani

Department of Electrical Engineering

Institut Teknologi Sepuluh Nopember

Surabaya, Indonesia

[email protected]

Surya Sumpeno



Surabaya, Indonesia

[email protected]

Mauridhi Hery Purnomo



Surabaya, Indonesia

[email protected]

Abstract— Twitter is one of microblogging service that

widely used by people. Its popularity invites spammers to

disturb other users with a large number of spam tweets.

Spammers send untrusted news, unwanted tweets to another

twitter accounts to introduce a product and service, a job with

high salary, promote a new website, spread advertise to

generate sales that could harm other users. This paper collects

a hundred accounts from non-spammer and spammer. After

that, manually classified as a non-spammer and spammer.

User's behavior characteristics, which could give many clues to

classify spammer. This paper applies profile users as features

for the machine learning to classify users as a non-spammer or

spammer. This paper applies seven attributes such as the

statuses count, followers count, friends count, the age of

account, average tweets per day, average limits between tweets,

verified user or not. Using a Decision Tree method, we could

classify non-spammer and spammer. The accuracy of the

classification of non-spammer and spammer is 88,235%

Keywords— decision tree, classification, machine learning,

microblogging, spammers, twitter.

I. INTRODUCTION

Twitter is one of microblogging service that widely used by people [1]. Twitter has a facility for users to post text, image, and video. Once post, users could type not more than 140 letters. People post news, poetry, achievement, feeling, or a link. They need to connect to colleague, friends, expand the professional network and more. By the time, Twitter becomes the fastest growing of microblogging service among all.

In another side, the popularity if Twitter also invites spammers to disturb other users with a large number of spam tweets. Spammers send untrusted news, unwanted tweets to another twitter accounts to introduce a product and service, a job with high salary, promote a new website, spread advertisement to generate sales, drug sales, disseminate pornography, viruses download that could harm other users [2]. To handle the spread of spammers, Twitter has a facility to report the suspicious user in one click "report tweet" that available in each user's page. After that, Twitter will investigate the reported user and will suspend the account if it is harmful to others [3]. Twitter has many rules for every user that use Twitter's service and one of the rules is not to spam anyone [4].

The data in this research are crawled use API Twitter [5]. A hundred accounts from spammer and non-spammer are collected. There are seven attributes to facilitate spammer classification to identify the user behavior of spam accounts. The Decision Tree method is used to classify spammer users from normal users.

Twitter's spam policy said, "if you have a small number of followers compared to the number of people you are following", then you could be identified as a spammer [6]. Spammers have a big effort to follow much more users to acquire their interest.

This paper is composed of Section II is about microblogging Twitter and discuss the work that related. Section III presents the attributes to identify the user behavior of spam accounts. Section IV presents the user behavior attributes. Section V describes the performance of spammer classification method and the result. The conclusion is in section VI.

II. RELATED WORK

A. The Twitter Social Media Site

There are many microblogging services widely used by people. Like Facebook, MySpace, and Twitter. Twitter has a facility for users to post a short text, it is called tweets. The text must be not more than 140 letters. The users have their own name (username) for each account that she made.

Twitter user A follows user B. In other words, user A is a follower of user B. User B could follow back or not. It depends on his own wishes. User B is following user C. User C is following back of user B. User B and user C is followed by each other. User B and C are friends.

User B is a follower of user C and user C is the follower of user B. User B could post a message that will appear to all of her followers. If you follow someone, everything of the message that has posted/retweeted by someone will appear to your Twitter's page. A twitter graph example is in Figure 1.

Figure 1. A simple Twitter graph [3]


4

An Image Processing and Artificial Intelligence

based Traffic Signal Control System of Dhaka

Abu Salman Shaikat



Dhaka, Bangladesh

[email protected]

Rezwan-us Saleheen



Dhaka, Bangladesh

[email protected]

Rumana Tasnim



Dhaka, Bangladesh

[email protected]

Rayhan Mahmud



Dhaka, Bangladesh

[email protected]

Farhan Mahbub



Dhaka, Bangladesh

[email protected]

Tohorul Islam



Dhaka, Bangladesh

[email protected]

Abstract— Traffic jam is one of the greatest problem of

Bangladesh. It affects mostly on its capital city, Dhaka, where

density of population is second highest among the world. One

of the major reason for occurring traffic jam is inaccuracy of

the use of traffic signal. This paper introduces an intelligent

traffic control system for four nodes traffic system. This system

is entirely controlled by the use of image processing and

artificial intelligence techniques. Image processing leads for

detecting the density of vehicles by using Haar Cascade

method, whereas artificial intelligence helps to modify the

timing of traffic signal accurately time by time. These process

held automatically and police can monitor from police box all

over the time by computer. Moreover, in case of emergency, a

manual system is introduced, which can support traffic police

to turn the system to manual and operate the timing manually.

Finally, traffic data is collected from road and prove the

effectiveness of proposed system. This system will support as

an extremely effective, self-coordinated and self-organized

traffic control appliance.

Keywords—Image Processing, Artificial Intelligence, Haar

Cascade, OpenCV, .net framework, Arduino

I. INTRODUCTION

For the big and crowded city like Dhaka, traffic

congestion is an irony of fate. Only 7% of its area is used for

road, whereas in every modern cities, around 25% of area

should be distributed for uses of roads. One of the main

reason of traffic congestion in Dhaka cities is lack of

planning for traffic signal. The traffic signal of Dhaka city is

traditional and its signal time is fixed for every routes. So,

traffic police can’t use these traffic signal nowadays.

Therefore, we introduced a new traffic control system for

Dhaka city, which will help the city from traffic congestion

issue by frequent changing the time of the signal in different

routes.

Viola and Jones introduced a system, which is called as

Haar Cascade method [13]. It’s used for detecting the

object, which superimposes positive image over a set of

negative images. High quality cameras are better to use for

this method. We introduced this method for detection and

counting the number of vehicles, which have been carried

out for four nodes [12]. OpenCV and .net framework 3.5

software’s are used for Image Processing.

By the data of cameras in different nodes, traffic signal

time will modifies time to time, which minimizes the traffic

jams [3]. Arduino helps to modify the timing of signals by

the camera image of four nodes.

A manual system is introduced in case of emergency.

Police can turn the system in manual mode and operate the

signal manually.

II. RELATED WORKS

A close study of the literatures revealed that several

researchers worked on intelligent traffic signal methods.

Darcy Bullock et al. reports on the advancement of new

vehicle detection system used by image processing. They

introduced neural network of feed forward/ simple back

propagation methods [1]. Liang-Tay Lin et al. proposed a

versatile traffic controller that use image processing for

vehicle detection and exploit artificial intelligence for traffic

control. However, they didn’t experimented with it [2].

Khaled Abdul Rahman Jomaa presented a system, which

can capture the information of traffic from camera and

adjust timing of traffic lights by using Artificial Intelligence

[3]. Yu Yuan et al. experimented with algorithm of camera

calibration for traffic detection. The researchers were able to

create the high-accuracy camera model [4]. Amoeba T.S.

chang proposed a system which includes neural network,

that uses for phase generation and changes of timing

anytime [5]. Mr. Somashekhar G.C. et al. presented an

intelligent technique that can uninterruptedly have an eye on

any movement happening everywhere and on

administrator’s demand, suspect can be tracked by image

processing [6]. Takashi Nakatsuji et al. used Kohonen

Feature map model and multilayer model for improving

estimation precision and computational efficiency. For

changing traffic signal, researchers used genetic algorithm

and Cauchy algorithm [7]. Xiangjun et al. used machine

learning and fuzzy theory for traffic signal control. Fuzzy

clustering used to count the number of cars. The genetic

algorithm is used to control timing of traffic signal [8]. Al

Hussain Akoum et al. implemented smart traffic controller


5

Conceptual Model of Household Consumer Behavior

in Storing WEEE

(Waste Electrical and Electronic Equipment)

Robby Marlon Brando* Department of Industrial Engineering


Depok, Indonesia *[email protected]

Romadhani Ardi Department of Industrial Engineering


Depok, Indonesia

[email protected]

Abstract— Rapid technological developments make the level of

consumption of electronic goods increasing. Unfortunately, the

increasing amount of consumption sets a large amount of

WEEE (Waste Electrical and Electronic Equipment).

Consumers often store electronic items that are not used in

storage at home. Consumer behavior to store WEEE at home

such as a time-bomb, sooner or later must be released because it

will be a danger. Uncertainty in the quality and quantity of

consumer storage at home that cannot be predicted makes it

necessary to analyze their behavior. This study aims to develop

conceptual model of household consumer behavior, especially to

find out the habit of storing WEEE at home. This conceptual

model is based on the Theory of Planned Behavior (TPB) which

assumes that a number of reasons or forming factors including

attitudes, subjective norms, and perceived behavioral control

are involved in the formation of intentions to carry out certain

behaviors. The output derived from this study is a conceptual

model of household consumer's behavior in saving WEEE that

validated only by pilot survey with 42 respondents. This

conceptual model still requires further validation with larger

sample.

Keywords—WEEE, Theory of Planned Behavior (TPB),

Structural Equation Modelling (SEM)

I. INTRODUCTION

With the rapid development of technology today,

electronic equipment has taken position as part of human life.

Almost all human activities are inseparable from the help of

electronic equipment. Households become very depends on

the use of equipment such as cell phones, laptops, televisions,

DVD players, washing machines, refrigerators, microwave,

ovens, and others with the aim of making life easier and more

comfortable. In terms of communication, people use cell

phones and telephones to communicate, while others are

comfortable using computers with internet connections to

transfer and receive information and share their knowledge

with the whole world. There has been a large market demand

for new electronics needed by manufacturing companies to

increase their production and people enthusiastically buy the

electronic equipment produced. The electronics industry is

growing faster and larger in the world. Unfortunately, the

increasing amount of consumption sets a large amount of

WEEE.

Electronic waste or commonly called WEEE is an

electronic product that is no longer used and has entered into

the waste stream or waste stream [1]. Used electronics that are

reused, resale, recycled, or disposed are also considered as

waste or WEEE [1]. Unused electronic devices or WEEE has

become a global issue that threatens human life, because it is

the fastest growing waste in the world today [2]. Estimates of

WEEE recycling rates vary by region. According to the data,

it is estimated that only 25% of WEEE in the European Union

[3] and 39.8% in the United States [4] are recycled every

year, while the rest becoming untraceable. At the global level,

the number of WEEE in 2016 is around 44.7 million tons,

estimated to reach 47 million tons in 2017 and the number

will increase to 52.2 million tons in 2021 with growth of 3% -

4% per year [5], but the management of WEEE technology,

especially in new industrial countries, is still at an early stage.

WEEE is a problem that affects consumers in two ways,

namely when buying new electronic products to replace the

old ones and when handling old products [6]. In the problem

of WEEE, consumers can be divided into two parts, namely

consumers as customers (users) and consumers as WEEE

holders (disposers) [6]. There is no need to discuss the

efficiency of WEEE management only from point of view of

disposal, consumption is also an important point, because the

increasing amount of consumption establishes a large amount

of WEEE [6].

Consumers often store electronic items that are not used in

storage at home [7]. This WEEE is not immediately

discarded, is not repaired if there is damage, or is not

immediately sold back to the second hand market. Even

though there are actually many dangers that can occur from

WEEE storage, considering the number of hazardous

substances contained in it, especially if the method of storage

is not adequate [8]. In addition, the hibernation of WEEE

inside the house makes the flow of WEEE management

system not work well.

In WEEE management systems, consumers in several

countries do not have legal or financial responsibilities [6].


6

Understanding the Structure of Policies in the

Future Implementation of Internet-Protocol-based

Interconnection in Indonesia

Irma Handayani

Department of Industrial Engineering

Faculty of Engineering,

Universitas Indonesia,

Salemba, Jakarta Pusat, Indonesia

[email protected]

Komarudin*




Depok, Jawa Barat, Indonesia

*Corresponding author

[email protected]

Akhmad Hidayatno




Depok, Jawa Barat, Indonesia

[email protected]

Abstract—With the benefits for the customer and long-term

efficiency for mobile operators in Indonesia through the

implementation of Internet Protocol-based Interconnection

between mobile operators, Government of Indonesia plans to

set up a policy to push the adoption of IP-based

Interconnection. However, the mobile operators that already

heavily invested in the current technology are worry that the

IP-based Interconnection will incur higher additional costs and

will lead to declining revenue due to the facts that their voice

and mobile-text revenue has continued to decline as a result of

the provision of substituted similar voice services by OTT (over

the top) application. This research developed a qualitative

conceptual model of policies that can be carried out by the

Government to support IP-based interconnection plan using a

system diagram. The conceptual model shows mobile operators

can implement IP-based Interconnection with the support of

OTT regulation, revisions of cellular tariff regulations,

revisions of interconnection regulations, QoS regulation for

mobile data services, socialization to encourage the use of

VoLTE services and publicly available of affordable VoLTE

smartphone.

Keywords— IP-based interconnection, VoLTE, system

diagram, telecommunication policy

I. INTRODUCTION

In recent years, telecommunications sector in Indonesia has shown significant development in mobile services offered by operators, previously only voice and SMS services; now it evolves to various applications through internet services which are supported by 3G and 4G networks expansion. In 2017, 3G and 4G BTS of big three operators (Telkomsel, Indosat, and XL Axiata) increased 15 – 40% YoY [1].

Even though 4G network deployment has provided a higher speed of data access and has increased data traffic more than 100% during last four years, mobile operators that have telecommunications services licenses must still provide voice services. Although mobile operators have built 4G telecommunications networks based on all-IP (Internet Protocol), voice services offered are still circuit-switched based on 2G / 3G networks. A result of the existing regulations which do not cover interconnections of voice services based on the all-IP network. In order to cope up with the new technological trend, in 2018 the Indonesian Government proposed to implement IP-based interconnection.

Despite better user experience for customers and efficiency in the long run for operators, the implementation of IP-based interconnection requires a substantial

investment [2], which raises concerns for mobile operators in Indonesia that the implementation of IP-based interconnection will incur higher added costs and will lead to declining total revenue. Based on the fact that extensive use of OTT services increases data traffic and substitutes voice and SMS services, which in turn lowered those revenues significantly.

This research aims to develop a qualitative conceptual model to capture the dynamic systemic structure of interconnection problems and analyze the government policy that can be carried out to implement the IP-based interconnection. We will use causal loop diagrams to represent the structure, which commonly used at the beginning of system dynamics modeling methodology.

II. LITERATURE REVIEW

This section presents a brief overview of Interconnection

in Indonesia including the need for migration towards IP-

based interconnection, then discuss VoLTE opportunities

and challenges and the last, policy analysis in the

telecommunication sector.

A. About Interconnection

According to Telecommunications Law No. 36 of 1999, Government Regulation No. 52 of 2000 on Provision of Telecommunications and Ministerial Regulation No. 8 of 2006, Interconnection is the connection between telecommunications networks from different telecommunications network operators. The telecommunications network operators defined as provider of circuit-switched fixed network, provider of cellular mobile networks and providers of mobile satellite networks. The International Telecommunication Union (ITU) defines interconnection as a set of legal rules, technical arrangements and operational agreements between network operators that enabled customers connected to a network to communicate with customers from other networks [3].

B. TDM-based Interconnection

Nowadays interconnection in Indonesia is still based on Time Division Multiplexing (TDM), which refers to Fundamental Technical Plan (FTP) 2000 regarding national telecommunications development. All interconnected networks use the same signaling standard, SS7, E.164 numbering scheme, with TDM transport media and 2 Mbit / s PCM digital interfaces or multiples using 64 Kbit / s A-Law encoding by ITU-T Recommendations G.703, G.704,


7

Understanding the Dynamics of Using Tobacco

Excise as an Earmarked Fund for Financing

Universal Health Coverage in Indonesia

Teuku Naraski Zahari

Industrial Engineering Department,

Faculty of Engineering

Universitas Indonesia, Kampus UI

Depok 16424

Indonesia

Email: [email protected]

Akhmad Hidayatno*




Depok 16424

Indonesia

Email: [email protected] *Corresponding Author

Komarudin




Depok 16424

Indonesia

Email: [email protected]

Abstract — Universal Health Coverage (UHC) is a public

health funding scheme promoted by the World Health

Organization (WHO) to achieve equity in healthcare service

including promotive, preventive, curative, and rehabilitation care

at an affordable cost. UHC also aims to provide financial equity

and financial protection for the community. In 2014, Indonesia

started the UHC program through its national program Jaringan

Kesehatan Nasional (JKN). In its implementation, JKN has faced

many issues with the most recent topic being the fiscal deficit in

JKN. This issue has come to national government attention

because the law states that the government must assure social

protection to its people. To counter this issue, the Indonesian

Government opt for a policy to expand fiscal space for public

health expenditure through an earmarked fund from tobacco

excise. Through qualitative conceptual model developed for

system dynamics, this paper provides a structural insight that

earmarked fund from tobacco excise, in Indonesia case, is not

sustainable in the long run. This unsustainability is mainly

caused by the adverse effects of tobacco use to health which in

turn increases public health expenditure and by its

socioeconomic effects which also further burdens the

government budget.

Keywords— Universal Health Coverage, Health Economics,

Fiscal Space, Public Health Expenditure, System Dynamics

I. INTRODUCTION

Fiscal space has been one of the central issues in Universal Health Coverage (UHC) program. Barroy et al. (2016) stated that this issue is particularly important in Low-Middle Income Country (LMIC) in which sustainability is a major concern rather than expansion. Aside from the obvious conducive macroeconomic conditions and budget re-prioritization, the earmarked fund could also be used to generate fiscal space for health [1]. In terms of sustainability, however, the outcome of the earmarked fund is questionable, although in some cases the result can be effective [2].

Based on its revenue source, there are three broad groups for the earmarked fund: general taxes, consumption taxes, and other instruments [2]. Indonesia utilizes general tax and consumption tax from tobacco excise as a revenue source; each poses different challenges. Tobacco excise as an earmarked source for health becomes an interesting subject as several papers have discussed contradicting outcomes. Tobacco excise offers potential in generating fiscal space for health expenditures. Peru and Gabon utilize tobacco excise to resolve their fiscal space issue. Both cases showed a small amount of fiscal space generation; Peru was able to

generate 0,02% and Gabon 0,05% [3]. In the case of Indonesia, however, resorting to tobacco tax excise may not be sustainable as discussed further in the next section. Through a qualitative conceptual model developed in system dynamics, this paper aims to provide a structural insight that earmarked fund from tobacco excise, in Indonesia case, is not sustainable in the long run.

In the following section, this paper presents the literature reviewed in supporting this paper. On the third section, this paper presents the conceptual model used in this paper. The insight from this model is presented in the fourth section followed by discussion and conclusion in the following chapters.


Tobacco excise tax for financing UHC sustainability is questionable [4]. There are three issues surrounding health financial sustainability: government resources constraints, the increase of health expenditure due to factors affecting demand and supply of health services, and the ‘overcrowding’ of health expenditure in total government expenditure [5]. The first two characteristics are most relevant to Indonesia case.

Indonesian government faces financial resources constraint in executing JKN. This constraint, however, does not necessarily represent macroeconomic conduciveness but rather a matter of willingness and prioritization. Indonesia has the 2nd largest GDP among LMICs (World Bank, 2017). However, its public health expenditure only covers around 38% of its current health expenditure, while, on average, other LMIC governments covers 52% of their current health expenditure. Although increased, Indonesia’s current health expenditure proportion also remained low to GDP at 3.12% in 2016 (World Bank, 2016). This proportion is much lower compared to the 6% recommendation from WHO [6] and even compared to neighboring LMIC such as Vietnam and Cambodia (World Bank, 2016) as shown in Figure 1.

Another constraint is the current tobacco excise rate of 52,4%, is approaching its limit of 57% as mandated by Indonesian constitution number 39/2007; indicating a limited potential in fiscal space generation. The number is lower compared to the 70% recommended excise rate by WHO [7].


8

Exploring the Policy Structure of Aircraft Industry

Development in Indonesia: A Conceptual Model

Isnaeni Yuli Arini



Depok, Indonesia

[email protected]

Komarudin*



Depok, Indonesia

[email protected]

*Corresponding author

Akhmad Hidayatno



Depok, Indonesia

[email protected]

Abstract—Aircraft industry is a complex high-tech industry

with many interconnected elements. A change in any element

would affect the entire system, which means that policy in only

a single sector or process may generate a less significant result

or even very contrast consequences in somewhere else in the

system. Therefore, an effort to create a better policy for

supporting the aircraft industry should implement the holistic

view of its industrial system. However, there is still not enough

academic reference or research discussing the system of the

aircraft industry. This research studies the industrial system

structure of the aircraft industry in Indonesia with the help of

Qualitative Conceptual Model found in the System Dynamics

approach. The built model consists of 10 primary processes in

the aircraft industry: Managerial, Financial, Skilled Labor,

Infrastructure, Design & Engineering, Test & Certification,

Production, Collaboration, Sourcing, and Sales. The conceptual

model shows the current system structure including the actors,

external inputs, and current government support to improve the

aircraft industry in Indonesia.

Keywords— conceptual model, system dynamics, aircraft

industry

I. INTRODUCTION

Indonesia started its effort to build the national aircraft

industry, formally, since 1976 with the inauguration of PT.

Industri Pesawat Terbang Nurtanio (IPTN). However, in

1998, a monetary crisis happened in the country, resulting in

the government cut its investment to the company. Afterward,

the Indonesian aircraft industry experienced a significant

downfall for more than a decade. Despite the financial

difficulty, the company refused to be taken down and kept

struggled for survival. In 2011, the IPTN, which has been re-

branded as PT. Dirgantara Indonesia (PTDI) and began its

Restructuration and Revitalization (RR) strategy with the key

strategy was financial restructuration [1]. The financial

restructuration showed a good result when PTDI finally could

end its cash flow deficit at the end of 2011, and the

government decided to reinvest in the company. However, the

problem of the aircraft industry in Indonesia is not merely a

financial situation. Shortage of skilled labor, technology gaps,

out-of-date infrastructures, are among other problems that

threaten the industry for the long run [2].

An aircraft industry is a high-tech industry with

distinguishing characteristics compared to other commercial

industries. Notably, the aircraft manufacturing industry needs

massive investment, yet the economic return is considerably

low, and the risk of failure is quite high [3] [4]. For example,

Indonesia once had a promising program called the N-250

with fly-by-wire cut-off technology. However, despite a

massive US$185 million funds from the government, the

program failed due to various reasons. The situation showed

that merely a substantial investment is not enough to build a

healthy aircraft industry.

Further government support in the form of policies in

several sectors is needed to build a healthy aircraft industry [2]

[5]. Moreover, the aircraft industry in Indonesia is part of the

strategic industry, which means that the industry is essential

for the development of the country, especially in the

technological sector. However, there is a question arise, if

such large funds in the past still not enough to make a robust

industry, what kind of policies that can help it to grow?

This paper aims to explore a better policy structure that can

help the development of the aircraft industry in Indonesia. The

exploration is started by understanding the aircraft industrial

system structure with the help of Qualitative Conceptual

Model found in the System Dynamics approach. To

understand the industrial system structure is necessary since

aircraft industry is a complex industry with many

interconnected elements [6], and in such a complex system,

policies that only have single direction to a sector or process

may resulting a not enough significant effect or even a very

contrast consequences in somewhere else in the system [7].

Although a holistic view of the aircraft industry system is

needed when exploring the policy structure, still very little

research or academic reference studied the aircraft industry

thoroughly in the last decades. Therefore, this research is done

to fill the gap in the literature.

This paper begins with a literature review and study of past

researches about Indonesia aircraft industry to collect

narration of the aircraft industry situation. The narrations

collected from the past researches include the systems

components of the aircraft industry, their possible structural

interrelationships and the context that gives meanings to these

relationships. The narration then used as qualitative data to

construct the conceptual model. The conceptual model

showed the causal diagram of the system, the system actors,

and existing policies. The model then used as the basis of the

discussion of the better policy structure for the development

of Indonesia aircraft industry.


A. The complexity of the Aircraft Industry

The aircraft industry is a unique commercial industry due

to its combination of unique characteristic [3]. One of the

standout characteristics is its continuous need for cut-off

technology and skilled labor. In the early times of IPTN, the

company depended on the license agreement and joint

venture with the more established aircraft companies for


9

An Improved Pupil Detection Method under

Eyeglass Occlusions

Sabrina, Sunu Wibirama*, Igi Ardiyanto

Department of Electrical Engineering and Information Technology

Faculty of Engineering, Universitas Gadjah Mada

Yogyakarta 55281 Indonesia

Abstract—There are various challenges of detecting pupil

during eye tracking, such as changing illumination conditions, occlusion of eyelashes or eyelids, obstruction of prescription glasses, poorly recorded images, highly off-axial positions, and so forth. Prior state-of-the-art method namely ExCuSe undertakes these problems based on analysis of histogram intensity. However, ExCuSe fails to analyze some pupil images with poor illumination and light reflection occlusion caused by prescription glasses. To overcome this problem, this research proposes an improvement in ExCuSe by incorporating two image filtering techniques in the preprocessing step. The median filter is utilized to diminish noise while the guided filter is implemented to preserve edges in the image. We evaluated the improved and the state-of-the- art algorithm on over 16,000 hand-labeled images in three data sets that contain eyeglass occlusions. The experimental result of data set III shows that the proposed method significantly outperformed the state-of-the-art algorithm with a 22.53% higher

detection rate (p<0.05). Although implementation on the other two data sets did not achieve a statistically significant result, the overall performance of the proposed method was still better than the state-of-the-art algorithm. Our study indicates that the proposed method is more sophisticated to handle poor illumination and light reflection occlusion compared with the prior state-of-the-art technique. In future, the proposed pupil detection method can be implemented in an eye tracker for interactive systems as well as for passive monitoring system.

Index Terms—Eye Tracking, Computer Vision, Pupil Detec- tion, Eyeglass Occlusion, Median Filter, Guided Filter.

I. INTRODUCTION

The principal symptom of the vestibular disorder is visual

vertigo—a dizziness caused by a motion of the visual environ-

ment or delusion of visual prompts due to a sensory confusion

or functional shambles [1, 2]. Visual vertigo is commonly

found as one out of several symptoms of Visually Induced

Motion-Sickness (VIMS)—a physical discomfort during expo-

sure of dynamic multimedia scenes. Vertigo during VIMS can

be treated by engaging intentional gaze fixation at one point

during exposure of provoking scenes [3–5]. Prior studies by

Diels et al. [3] as well as by Wibirama and colleagues [4, 5]

have implemented eye tracking technology as a behavioral

analysis device to understand how voluntary eye movements

is useful to reduce the occurrence of VIMS.

*Corresponding author. Tel.:+62-274-552305. Address: Intelligent Systems

Research Group, DTETI Bld., Jalan Grafika 2 Yogyakarta 55281 Indonesia.

Email: [email protected], {sunu, igi}@ugm.ac.id

On the other hand, an embedded eye tracking system has

been found to be useful to support more realistic 3D content

rendering in a head-mounted display Virtual Reality (VR)

system. An experiment conducted by Hillaire et al. [6] shows

that eye-tracking is a useful tool to retrieve a user’s focal point

by adopting two rendering techniques, namely camera motion

and Depth-of-Field (DoF) blur effect. By such techniques,

high-quality contents are rendered only at the location where

the user gazes at—allowing faster rendering process and higher

frame rate. In this case, an eye tracker is used to support

various interactive VR applications.

The accuracy of gaze estimation and eye movements de-

tection profoundly rely upon the accuracy of pupil detection.

There are numerous studies on pupil detection. However,

most of the cases are under laboratory conditions [7–9]. An

example of studies focuses on pupil detection under laboratory

conditions is the algorithm of pupil localization under eyelid

occlusions proposed by Satriya et al. [7]. This method utilizes

an improved ellipse fitting as the proposed method, Random

sample Consensus (RANSAC) outlier removal in pupil contour

extraction, and moving average filtering in video processing.

The proposed algorithm was used for eye tracking during

high occlusion condition to improve the accuracy of a Video-

Oculography (VOG) system. A similar objective has been

proposed by Setiawan et al. [8]. They propose an improved

algorithm that surpasses its predecessor to localize the pupil

with more than 70% coverage on the pupil.

Another pupil detection algorithm is proposed by Goni

et al. [9]. Their algorithm calculates the threshold based on

histogram analysis to search bright region. However, the most

well-known pupil detection method under laboratory setting

is Starburst algorithm [10]. This algorithm aims to search

convergence of ellipse fitting by calculating the mean position

repetitively from the difference consecutive pixels that higher

than the threshold.

While pupil detection technique under tight experimental

control is obviously useful for some particular applications,

the developed image processing methods are vulnerable if

the experiment is conducted in real-world scenarios. There

are many challenges for detecting pupil under real world-

scenarios, such as changing illumination conditions, the inter-

sections of eyelashes or eyelids with the image of the pupil,

glasses, poorly recorded images, highly off-axial positions, and


10

Total Quality Management Implementation in Small

Business: Case Study in Depok, Indonesia

B H. Purwojatmiko, M. Rois, L. Ambarwati, A. Fauzan, P. D. Rahayu, R. M. Brando, A. Adilla, M. Y. Ilham, R. Nurcahyo*,



Depok, Indonesia

Corresponding authors: *[email protected]

Abstract—this research focuses on the implementation of

TQM in small businesses especially restaurant or food stall in

Depok, West Java. There are three aspects of TQM that are

used as indicators of research: Customer Orientation,

Continuous improvement, and Employee Empowerment. The

result shows that TQM concept not fully implemented on

Warung Tegal and Warung Padang. This condition due to not

all criteria can be adopted properly in practice. From the

research we could see from the costumer orientation category,

4 out of 10 questions are scored below 2. Customer orientation

is very important because quality is defined by customer.

Gathering information about what the customer wants on a

regular basis is needed to make improvements. For continuous

improvement criteria, the average score 2.91 out of 5 from 5

questioner’s questions, which shown that most of the food stalls

didn’t fulfil the criteria of TQM application in continuous

improvement. For the employee empowerment category, a

third of the Warung Tegal and Warung Padang recruit’s

employee from relatives with may or may not has something to

do with quality because they accept employees based on family

not skills and abilities.

Keywords—TQM, Small Business, Customer Orientation,

Employee Empowerment, Continuous Improvement

I. INTRODUCTION

According to Government Regulation No. 9 Year 1995

[1], a small business is a small-scale economic activity of the

people with specific criteria: (a) has a net initial capital

maximum of Rp 200,000,000, (approximately USD 13,000)

not including land and buildings; (b) has an annual sales

revenue of less than Rp. 1,000,000,000 (approximately USD

USD 66,000); (c) is owned by Indonesian citizens; (d) is a

stand-alone or a subsidiary or a branch owned by, controlled

by, or affiliated directly or indirectly with a medium or large

business; and (e) is in the form of an individual business, a

business entity that is not a legal entity, or a business entity

that is a legal entity, including cooperatives. Meanwhile, the

World Bank Group in a report titled "The SME Banking

Knowledge Guide" defines a small business as having less

than 50 employees [2]. Small businesses, or more commonly

known as Micro, Small, and Medium Enterprises (SMEs),

have a unique role in developing countries, such as job

creation, contribution to revenue, and ensuring the

distribution of limited resources [3].

In Indonesia, SMEs proved to be unaffected by the

crisis in 1997-1999; the number of SMEs after the 1997-

1998 economic crisis did not decrease, but instead

increased. The SMEs sector's contribution to gross domestic

product (GDP) also increased in the last 5 years. In the

records of the Ministry of Cooperatives and Small and

Medium Enterprises (SMEs), a contribution of 57.84% in

2015 rise to 60.34% [4]. Following the illustration of total

SME in Indonesia from 2009 – 2017 (Fig. 1).

Fig. 1. Increasing number of SME in Indonesia (Source: State

Minister for Cooperative Small and Medium Enterprise)

SMEs have a proportion of 99.99% of the total number of

businesses in Indonesia [5]. In terms of employment, SMEs

were able to absorb 10.7% or 12 million people in Indonesia

work in the SMEs sector [4].

Many SMEs in Indonesia are traditional businesses with

low productivity. Most SMEs produce basic goods, with

low value added for the local market [6]. One of the

challenges faced by SMEs in this regard is lack of

knowledge about the latest production technologies and how

to conduct quality control of the product [5]. Various

research has been discussed approaches to improve quality

control, thereby increasing efficiency and competitiveness

in the SME industry by improving quality. The most

commonly used approach to quality improvement is total

quality management (TQM). TQM is an approach that seeks

to integrate all functions that are focused on meeting

customer and organizational needs [7].

This research focuses on the implementation of TQM in

small businesses, especially restaurant or food stall in

Depok, West Java. Based on data from the Central Bureau

of Statistics (BPS), the number of restaurants or food stalls

in the West Java province has increased from 2,714 in 2013

to 2,853 in 2016 [8]. The number of stalls is certainly the

overall amount of food stalls, from small scale to large

scale. The basic research question is how small business

implementing TQM.


11

Model Conceptualization for Optimal Strategies in

Transboundary Movement of Waste Electrical and

Electronic Equipment: A Game Theory Approach

Pilamupih Dwi Rahayu




Depok, Indonesia

Corresponding author:

[email protected]

Romadhani Ardi




Depok, Indonesia

[email protected]

Andri D. Setiawan




Depok, Indonesia

Department of Industrial Engineering &

Innovation Sciences

Eindhoven University of Technology

Eindhoven, The Netherlands

[email protected]

Abstract—Waste electrical and electronic equipment

(WEEE) is prohibited for export, while used electrical and

electronic equipment (UEEE) can be exported because they still

have valuable components. Transboundary movement of

WEEE occurs due to unclear boundaries between WEEE and

UEEE. As a consequence, many countries took advantage of

the opportunity to export their WEEE on behalf of UEEE. This

conceptual paper discusses the application of game theory to

WEEE and UEEE research by highlighting the strategy in

game theory in various aspects of the transboundary

movement. Game theory is one of the mathematical models

often used in dealing with environmental issues, but

there is no research that uses game theory to deal with

transboundary movement of electronic waste issue. Therefore, this research was conducted to fill the gap and

provide a broader understanding of game theory. For this

purpose, the first step is to describe the basic formulas in game

theory, their uses and limitations, and applications in

environmental problems. Then, examine several strategies for

WEEE and UEEE problems with the game theory. The

expected results of this study are to determine optimal

strategies for developed and developing countries and to

mediate countries involved in the transboundary movement of

WEEE. These strategies are expected to reduce transboundary

movement of WEEE between respected countries.

Keywords—WEEE, UEEE, Transboundary Movement, Game

Theory

I. INTRODUCTION

The development of information and communication

technology (ICT) and infrastructure in the world is

overgrowing. Nevertheless, along with the development of

ICT, the ownership of electronic products becomes very

complex. Such development makes electronics companies

continue to innovate by releasing many new products,

causing the society to replace electronic items in a speedy

period. This behavior makes the number of electronic waste

or e-waste in various countries increase. The problem of e-

waste is becoming more complicated with the presence of

transboundary movement of e-waste between countries. E-

waste or waste of electrical and electronic equipment

(WEEE), are all electrical and electronic equipment (EEE)

and components that have not been used to be discarded by

users without any intentions to use it again [1].

Transboundary movement occurs because unused WEEE

is classified as used electrical and electronic equipment

(UEEE) which has the potential to be reused, repaired, and

recycled [2]. Further, difficulties in classifying between

WEEE and UEEE make transboundary movement an

international environmental problem [3]. Also, management

of WEEE and UEEE in developed countries has been the

subject of public debate, due to recycling or disposal in their

own countries or exports to other developing countries [3].

As a consequence, many countries took advantage of the

opportunity to export their WEEE on behalf of UEEE.

Besides these reasons, another cause of transboundary

movement is the differences in policy regarding

WEEE/UEEE between developed and developing countries

[4]. This situation, which can worsen the problems of

transboundary movement of WEEE, needs to be scrutinized.

Understanding the flow of WEEE/UEEE, especially from

developed countries to developing countries is, therefore,

essentials in order to reduce the transboundary movement of

WEEE between such countries.

Transboundary movement of WEE can be seen as

complex strategic situations involving decisions of different

groups or parties with different strategic behaviors. Such the

decisions of groups affect each other’s. There are numerous

studies on transboundary movement of WEEE (e.g. [2], [3],

[4]). However, research focusing on the strategic behavioral

aspects between developed and developing countries to deal

with transboundary movement of WEEE is still rarely found.

In this regard, this conceptual paper aims to propose the

application of game theory as the basis for determining

optimal strategis to solve the problems and to mediate the

countries involved in the transboundary movement of

WEEE.


A. Transboundary Movement

Transboundary movement of WEEE to developing

countries attract considerable attention. Basel Action

Network (BAN) has conducted research on transboundary


12

Price Estimation Model Using Factor Analysis in

Procurement

Achmad Faizal

Industrial Engineering Department



Kampus UI, Salemba 10430, Indonesia

[email protected]

Zulkarnain




Kampus UI, Salemba 10430, Indonesia

[email protected]

Isti Surjandari




Kampus UI, Depok 16424, Indonesia

[email protected]

Abstract—Procurement as a vital sector for cost efficient of

a company must negotiate with vendors to get the best price for

procuring assets and services for a company. Part of

negotiation is creating price estimation by a purchaser. Price

estimation factors are debatable for procurement experts. This

research aims to determine dominant factors for price

estimation in a procurement and creating a model based on the

factor especially lease asset procuring. Leasing price estimation

factor is never yet on the research. In addition, the research

consider factors from previous research on price estimation

and add novelty factors as a consideration such as vendor

performance rating, quantity order, and tenancy of assets . A

confirmatory factor analysis is conducted in this study, because

some factors taken from research previously and additional

factors from some researchers but not yet be determinant of

estimation factor. This confirmatory factor analysis need to

test what indicators have been grouped according to latent

variables and find dominant factors can be considered for the

price estimation model. The confirmatory factor analysis

results shows that in price estimation model in leasing are

location, amount of competitor, and quantity order grouped as

one model factor predictor.

Keywords—Procurement, Price Estimation, Common Factor

Analysis.

I. INTRODUCTION

The issue of cost efficiency for spending goods and service is a problem all companies. Procurement department is one of the few sections within any organization that spends about 70% of the organization’s cash resources and therefore has a unique opportunity to reduce some of the organization’s costs and thereby increase sustainability [1] ence, it is reasonable to expect Procurement function has task not only spending budget of company for goods and service efficiently but also keeping quality delivery of goods and service properly. Objective of purchasing are to acquire the right quality of materials at the right time in the right quantity from the right source at the right time [2] , [3] Sometimes, those objectives cannot be fulfilled when negotiation with supplier is in a deadlock situation. On the other hand, a procurement function needs to fulfill service level agreement to accomplish a contract released.

Based on guidance of [4], stating that procurement

procedures can be through tenders, e-purchasing, online

stores, direct appointment, direct procurement, and fast

tenders. The Procedures on how to procure will affect of

lead time and service level agreement. Thus, business

dynamics in external company can affect lead time of

procurement. This research will focus on direct appointment

procurement, because the difficulty for negotiation is hard

to agree with vendor. Direct appointment procurement is

related to only one vendor who is able to do it or who has

privilege from planner to do it. Price estimation and negotiation is the most contributing factor of lead time in Procurement. Price estimation are influenced by many factors and accuracy of price determining. The accuracy of price estimation is more influential for preparation of early negotiation and supported by detail information of design requirement from planner or user. Exactness of price estimates will determine how much saving on expense can be made and how long negotiation will take a time for deal with vendor.

This Study will focus on how to determine most influential factor to procure rental of assets and create the model price estimation in the end. The factors are obtained from previous studies about price estimation in construction. Several new factors that contribute to the price estimation such as vendor performance rating, quantity order, and tenancy of assets because of leasing are then added for consideration. According to, [5] that rational behind quantity model is derived from the numerous economic advantages gained from customers ordering larger quantities of product. Moreover, [6] state vendor performance that past performance indicators have always affected the selection of suppliers. The last new factor is tenancy of assets, this research need to examine the factor of competitor companies that have leased the building. The assumption of that when the company lease similar location with competitor, any revenue split on the sale and the company and competitor can share expense of tenant.

Prior to create price estimation model, a factors

reduction is needed to be done through an factor analysis.

Factor analysis is suit for finding unobserved latent

variables and reduce a smaller number of latent variables.

This research will use especially confirmatory factor

analysis, because some factors take from previous research.

Confirmatory factor analysis will help to reduce which

factor is not correlated to price estimation in leasing

procurement. The latent variables or price factors which will

be formed, those factor will be recommended as the model

price estimation on procuring leasing asset.

II. LITERATURE REVIEWS

A. Factors affecting the price estimation

Some researches on price estimation usually happen in

construction project. Unfortunately, It is not yet happened

and discussed in leasing of assets procurement. This

research adopts some factors from previous research on

construction to determine which factor is the most

influential for price estimation. Based on [7] stated that

identifying the magnitude of the influential factors would


13

Sentiment Analysis of Standardization using Deep

Belief Network: a case of Indonesian National

Standards

Aries Agus Budi Hartanto




Kampus UI, Salemba 10430

Indonesia

[email protected]

Zulkarnain




Kampus UI, Depok 16424

Indonesia

[email protected]

Isti Surjandari




Kampus UI, Depok 16424

Indonesia

[email protected]

Abstract—Free trade era requires increasing the

competitiveness of local products in the global market, through

standardization. The standardization policy is including how to

plan, formulate, establish, implement, enforce, maintain, and

supervise National Standard, e.g Indonesian National Standard

called SNI. SNI is useful in order to create competitiveness and

consumer protection. The consistency of standardization shows

through standardization activity, that requires time and high

resources. The number of SNI and the breadth of products

distribution cannot be monitor simultaneously in the same

years, also another obstacle in standardization activities.

Therefore the aim of this study is to find a classification of

standardization activity, which to becomes an important part of

evaluation policy. The development of media plays a role in

policy making, information and opinions from the media can

change standardization's policy strategies. The contribution of

this research is using text mining from standardization

publication in media, to find useful knowledge. It's useful to

build an input alternative, in the form of media sentiment

analysis in standardization activity, that has never been done

before. It gives an agile method for dealing with rapid changes

in the standardization process. This study uses a deep belief

network (DBN) method for the classification of media

sentiment. Besides using DBN, this study also compares DBN

with other classification methods, namely Naive Bayes (NB) and

Support Vector Machine (SVM). These research results show

the accuracy of the classification model with DBN reaches 77%,

NB reaches 74% and SVM reaches 77%. Moreover, the results

show that the most negative sentiment is 19% and the most

positive sentiment is 29.20%. Both of the sentiments are the

member of class about implementation and the mandatory

regulation of SNI, and those aspects becoming media

concentration. Standardization situation is expected to be

captured as the output of this study so that it can contribute to

improving the standardization policy in Indonesia.

Keywords—standardization, competitiveness, SNI,

conformity assessment, text mining, sentiment analysis,

classification, deep belief network.

I. INTRODUCTION

The globalization and free trade era raise the main issues for increasing and strengthening the competitiveness of national products in the global market, holding back the aggressiveness of foreign products entering a country, and preventing the circulation of foreign products that endanger the security, health, and safety of the public [1]. With the development of the global economy, standards affect

competition, changing patterns and times, as in [2] and [3]. One of the things that are done to anticipate the current of globalization is by applying national standards, such as the national standards that have been recognized in Indonesia, namely the Indonesian National Standard (SNI). Strategies in market mediation can be carried out by applying standards [4]. Several developed countries such as Canada, Germany, Japan, Russia, Britain, and America have been implementing standards as national strategies, as in [5], [6] and [7]. Standardization is the process of planning, formulating, establishing, implementing, enforcing, maintaining, and supervising standards that are carried out in an orderly manner and in collaboration with all stakeholders [8].

Standardization is carried out to improve product competitiveness, streamlining trade flows, stem foreign products, as well as protect producers and consumers [9]. Therefore, there is a need for defining a mechanism to ensure that standardization activities run well. Some supervision and evaluation activities have been carried out such as picking tests by National Standardization Agency of Indonesia (BSN) [8] and supervision of the circulation of goods and services by the Ministry of Trade. Other activities are such as surveillance, testing, inspection, auditing, and certification by the conformity assessment body (LPK). This activity depends on the allocation of costs, the number of personnel and it takes time to produce a policy recommendation.

On the other hand, there are obstacles in standardization activities, including industrial unpreparedness in the implementation of national standards, the high cost of certification and testing, the limited number of assessment body and the number of products that need to be monitored, the standard technical specifications that are not appropriate, uninformed industry about national standards, the limited understanding of technical standards implementation, the increasing of goods circulation, and challenges from a weak national quality infrastructure as in [10], [11] and [12]. These constraints become a phenomenon that is often faced by developing countries [13] related to standardization, including Indonesia.

These constraints are highlighted by the media in standardization activities, hence a large number of publications both positive and negative on the performance of standardization can enable policy changes. Because the level of media sentiment is in the form of high complaints, lack of public understanding about the implementation of SNI, and freedom of opinion, those can lead to strategic


14

Scalable Data Analytics from Predevelopment to

Large Scale Manufacturing

Heiner Heimes

Chair of Production Engineering of E-

Mobility Components (PEM) of RWTH

Aachen University

Aachen, Germany

[email protected]

Anita Steinberger

BMW Group

Munich, Germany

[email protected]

Achim Kampker



Aachen University

Aachen, Germany

[email protected]

Joscha Eirich

BMW Group

Munich, Germany

[email protected]

Ulrich Bührer



Aachen University,

Aachen, Germany

[email protected]

Stefan Krotil

BMW Group

Munich, Germany

[email protected]

Abstract—Data analytics provides a toolset to extract

insights from large amounts of data. In order to stay

competitive, companies of the manufacturing domain utilize

data analytics to be more efficient and to increase quality of the

production and product. Current methodologies for the

application of data analytics and data mining techniques focus

on finding correlations within data from existing systems and

historic data. Therefore, data analytics is typically applied to

solve existing problems within existing manufacturing systems.

Since present brownfield production lines often provide

insufficient data, new hardware has to be retrofitted to acquire

the required data. Hence, valuable time for problem solving is

lost. This paper presents an approach to proactively implement

data analytics during early predevelopment phases in order to

allow scalability of the approach to large scale manufacturing

systems. The approach is implemented and evaluated within

the context of high voltage battery manufacturing for electric

vehicles.

Keywords—Data Analytics, Early Phase, Data Mining

Methods, Manufacturing

I. INTRODUCTION

Within the manufacturing industry, competition and process complexity are increasing [1–3]. The current approach to ensure high product quality and process stability demands comprehensive testing. These testing efforts require high investment costs for test rigs and result in higher production time demands. Therefore, increasing the knowledge about the manufacturing processes and their dependencies is essential as a basis to reduce testing efforts and to increase quality and efficiency of the manufacturing processes as well [4].

In this context, digitalization and data analytics are expected to provide the necessary toolset [5]. For this purpose, production as well as process data needs to be acquired and analyzed using data analytics methods such as CRISP-DM [6]. Today, costs for data acquisition and storage prevent the acquisition of all data generated in large scale manufacturing systems [7, 8]. Therefore, data analytics projects, which are initiated reactively to solve existing manufacturing problems, often lack the required data or data quality. This leads to increased costs and lost time due to retroactive data acquisition. Hence, previous work of the authors focused on proactive identification of valuable data

analytics use cases during early development phases of manufacturing systems [8]. This includes acquiring information about relevant process variables as well as the necessary acquisition quality. In order to identify an optimal implementation strategy, the use cases were prioritized based on expected benefits and costs [9]. Still, an approach is missing that facilitates a scalable implementation of prioritized use cases from early development phases to series production. This approach must tackle the challenge of small amounts of available data during prototype production and nonexistent data at the beginning of large scale manufacturing. Furthermore, the synergetic nature of data analytics use cases has to be considered.

Based on this challenge, this paper presents an approach, which enables proactive implementation of prioritized use cases during predevelopment stages and provides scalability to large scale manufacturing systems. Using this approach, identified use cases are implemented at an early prototypical stage, using specified interfaces to Industrial Internet of Things solutions. Basis for this method is to embrace the synergetic nature of data analytics use cases regarding data reuse 5from prototype to series production, common interfaces and cloud solutions. Furthermore, information gain and benefits of data analytics are leveraged on each development stage. This approach introduces a method with focuses on the following phases, adaptive data availability from prototype to series production, automated initial data evaluations, modeling and knowledge generation.

This approach was implemented and evaluated within the context of high voltage battery manufacturing for electric vehicles.

II. STATE OF THE ART

In order to provide an overview over existing methods for the implementation of data mining and data analytics, the most renowned methods are introduced in the following.

A. Existing Methods for Implementing Data Analytics

The most widely used method for data analytics is the Cross Industry Standard Process for Data Mining (CRISP-DM) [6, 10]. It describes an iterative and circular method with the phases Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and


15

Designing Theoretical Framework for Measuring Burnout related to Academic System Amongst

University Student

Julya Ade Jhora Amalia Suzianti Romadhani Ardi Industrial Engineering Department Industrial Engineering Department Industrial Engineering Department

Universitas Indonesia Universitas Indonesia Universitas Indonesia Depok, Indonesia Depok, Indonesia Depok, Indonesia [email protected] [email protected] [email protected]

Abstract—Universities have an important role in enhancing

the education of a nation, which aligned with efforts to create

qualified graduates. In relation to this circumstance,

universities generally establish several standards (apart from

the national standards established by the Ministry) in their

academic systems such as standard of competency, study

allocation for each semester, teaching and learning processes,

teaching period and any other related standards depend on

faculty and/department within the universities. However, those

established standards create the amount of demand and/study

burden, particularly for students. This paper aims to develop a

design framework and present the responsible factors that

related to academic systems, by the questionnaires that are

designed for monitoring the effect of the academic systems on

student burnout during their study. This paper focusing on the

relation of student burnout toward the academic system which

is analyzed with Partial Least Square (PLS) method. It is

expected that the output framework from this paper will

contribute in evaluating the academic system on higher

education and can be a guide for developing a better academic

system within the department university hence improving the

quality of student outcomes and prevent burnout.

Keywords—design, academic system, university, teaching and

learning, student burnout, conceptual model, questionnaires,

Partial Least Square

I. INTRODUCTION

University is an educational unit as a promotor of higher education. In Indonesia, it can take the form of academies, institutes, polytechnics, high schools, and universities. This educational unit can hold academic, professional, and vocational with diploma education programs, bachelor degree, master degree, doctoral, and specialists. Each university has its own standards in enhancing the qualified education for students, including a proper curriculum, good academic system, competent educators in their fields, comfortable environment of the university ting, and so on, with the expectation that students are able to adapt and attend the class until they graduated.

Students are populations that susceptible to burnout because they experience several socio-economic constraints, requirements of academic assignment (such as papers, tests, and examinations), personal, and social pressures related to lecturers and colleagues. On the other hand, they have no enough leisure time to spend with family as well as friends and may run into stress related to professional expectations on the future and expediency of their study [1].

In performing their study, students must fit into the

educational system, learning methods, and social skills which

are very different from their previous level of education [2]. They are also expected to be able to fulfill various demands on work assignments, dealing with the complexity of academic material that is more difficult in every year, doing social reconciliation in the university environment, and fulfilling the expectations of academic achievement [3].

Academic activities which have been done by lecturers and students are usually linked to a semester credit system where education is focused on the study load of students. The number of credit values for each course is determined by the effort in completing the tasks which are given in the subject program, practicum, field work, and other demands which is also required to fulfill the standard of competency which has been [4].

However, standardization of the credit system and the standard of competency will be involved in students’ excessive study loads. Academic systems that are less or not optimal can be affected by the number of academic activities, the density of course schedules and tasks that must be completed by students, both individual and group, and inappropriate deadline for collecting assignments can inflict burnout for students [4].

In Indonesia, there is less study on student burnout using

academic system approach in general context [4]. Several study

on student burnout in university was measured with

psychological or psychosocial approach like self-efficacy,

motivation, academic performance and their engagement in

university [23]. Therefore, this paper aims to develop a design

model framework for evaluating the academic system by

covering the factors that can affect the student burnout and can

provide a better development of the academic system that

prevents burnout among students in higher education. So, that

will be the contribution from this paper for measuring student

burnout in university with different approach.


A. Academic System

1) Academic Obstacles: Are characteristics that can

possibly deter or obstruct academic performance and

productivity while inducing burnout. Academic obstacles can

be personal, social or organizational and refer to those tangible

characteristics of the situation that has the capacity to obstruct

the performance. Examples of academic obstacles include

workload, anxiety, lack of information regarding tasks,

attending classes, writing exams, poor planning, insufficient

access to materials, searching for employment,


16

Analysis of Driver Acceptance Level Towards

Advanced Driver Assistance Systems in Jakarta

Ahmad Zaki Amalia Suzianti Romadhani Ardi

Department of Industrial Engineering Department of Industrial Engineering Department of Industrial Engineering Faculty of Engineering, Universitas Faculty of Engineering, Universitas Faculty of Engineering, Universitas

Indonesia Indonesia Indonesia Depok, Indonesia Depok, Indonesia Depok, Indonesia

[email protected] [email protected] [email protected]

Abstract—As one of the most populous capitals in the

world, Jakarta experiences rapid population growth every

year. It is followed by an increasing number of vehicles

rapidly. The problem arise when Jakarta was named as one

of the cities not comfortable to drive based on Driver

Satisfaction Index 2016 released by Waze [3,37 out of 10]

and around 98 thousands accidents occurred in 2017.

Advanced Driver Assistance Systems [ADAS] aims to

enhance driver performance and improve safety. ADASs can

intervene as needed when facing certain situations which it

will challenge the way of traditional driving. Therefore, the

purpose of this research is to measure driver acceptance

using ADASs to find out the level of driver’s intention to

adopt new in-vehicle technologies. Unified Model of Driver

Acceptance [UMDA] is used to investigate the factors that

affecting behavioral intention to adopt ADASs. As expected

result, the data of respondents then analyzed to propose the

adjustment of ADASs to fit driver’s expectation.

Keywords—Driver Advanced Driver Assistance System, Vehicle Automation, Technology Acceptance, Forward Collision Warning, Lane Departure Warning

I. INTRODUCTION

Technology advances is inevitable in every aspect.

Artificial intelligence takes an important role in technology

advances to help human tasks become easier. Car technology

without exception has already developed with remarkable.

The big dream of car manufacturing is to create the

autonomous driving systems which every car can

communicate each other, analyse the situation and makes a

warning or intervention as needed. The increasing number of

vehicles every year and the changing landscape of the road

makes this goal a new hope of a better way of driving. As

one of the most populous capitals in the world, Jakarta

experiences rapid population growth every year. It is

followed by an increasing number of vehicles as well.

According to Polda Metro Jaya (Regional Police of DKI

Jakarta), with number of population that reach 10,3 million in

2017, one of three of Jakarta citizen has at least one car.

Nowadays, Advances Driver Assistance Systems

(ADAS) has been embedded to cars that available in the

market. ADAS are intended to enhance driver performance

and improve transportation safety [4]. The potential benefits

of these technologies, such as reduction in number of

crashes, enhancing driver comfort or convenience, decreasing

environmental impact, etc., have been acknowledged by

transportation safety researchers and federal transportation

agencies [4]. ADAS provides many features such as warning

or alert and also can intervene the driver without taking the

role as a whole. The expectancy

of ADAS is to reduce human error whether it is incapable of control the car or attributes such as age, gender or driving behavior. Beside the functionality of ADAS there is a customer side that need to consider, because without a user full acceptance, the potential benefits of safety devices could be reduced because of incorrect use or adversity to their use [9]. In order to appropriate use of ADAS, it is important for customer to feel that the system will help them to impove safety and performance. Moreover, the customers need to know how the system works after they activate the button on. A. Driver Acceptance

Driver acceptance of ADAS can be defined as the reaction of drivers when they are exposed to an in-vehicle technology and their willingness to adopt the technology while driving [4]. Without the acceptance of driver, it could be when the system does its job well but the user feels annoyed which eventually disables the system so that the presence of the system becomes useless. It is therefore necessary to study driver acceptance to ensure the appropriate use of driver support systems [5]. Based on literature review, there are many factors which can affect driver acceptance. B. Advanced Driver Assistance Systems

ADAS is the technology of present and future that possesses potential growth in the market. In Indonesia automotive market, manufacturers has embedded their product with ADAS despite still limited in the middle-up segmentation. ADAS technology has many advantages, such as providing drivers with important information, relieving drivers by occasionally taking over parts of the driving task, and sometimes providing added control to aid drivers in critical situations [4].

Two features of ADAS is investigated in this study; Forward Collision Warning (FCW) and Lane Departure Warning (LDW). FCW is an active safety system that warns the driver after sensing the object in front to avoid the imminent collision. FCW uses cameras, sensors or both to scan the road ahead and warns the driver if the distance with the object in front are too close by ‘beep’ sound, indicator visual or vibrate the steering. LDW is a mechanism that steer you back into your lane if you begin to drift out of it. First when you drift out of the lane unconsciously it can give the warning e.g. horn or steer vibration, if there is no respond then the system will steer the car back into the lane.


17

An Improved Accident Analysis Model for The

Scheduled Civil Aviation Industry in Indonesia

Rhahadian Bima Saputra Amalia Suzianti Romadhani Ardi

Department of Industrial Engineering Department of Industrial Engineering Department of Industrial Engineering Universitas Indonesia Universitas Indonesia Universitas Indonesia

Depok, Indonesia Depok, Indonesia Depok, Indonesia [email protected] [email protected] [email protected]

Abstract— Civil aviation industry in Indonesia has been

nominated by some survey institutes to be the lowest in safety

rating in the world. This is caused by poor safety management

system which lead to many civil aviation accidents in

Indonesia. According to Maintenance Error Decision Aid

(MEDA), nowadays, 80% of aviation accident are due to

human error (pilots, air traffic controllers, mechanics, etc).

This result differ compared to the early years of the aviation

industry which is 80% of accident are caused by machine

failure. Therefore, we have to find the most appropriate

method to analyze an aviation accident in order to prevent its

reccurence. In Indonesia, scheduled civil aviation almost

represent all civil aviation in the country. Therefore, This

research proposed a modified accident analysis and

investigation model based on swiss cheese model to identify the

human and organizational factors involved in scheduled civil

aviation accidents. The model will be consist of categories and

subcategories which is developed by classic ancient causation

models combined with the laws and regulation in Indonesia

and a safety management system practices in the scheduled

civil aviation industry. The proposed model is expected to be

able to analyze scheduled civil aviation accident better and

clearer and help the management to take a safety action needed

to prevent the recurence of accidents.

Keywords— aviation safety, scheduled civil aviation accident,

swiss cheese model, accident analysis

I. INTRODUCTION

Indonesia is a the fourth most populated country in the world and with approximately 17.000 islands in it, Indonesia become the world’s biggest islands country. In recent years, with the boom of low cost airlines and overall economic develoment, aviation become more favored type of transportation that people tend to choose compared with sea transportation. Aviation has proven to be easier, more comfortable, faster, cheaper, and broader. Together with the growing of international tourism, Indonesia’s air traffic is rapidly growing too from around 27 million in 2009 to almost 76 million in 2015 [1]. The growth is almost 11% a year and is greater than the average air traffic growth in the world which is 7% [2].

There are two types of civil aviation, Scheduled civil

aviation (SCA) and general aviation. SCA is one type of civil

aviation that represents all non military aviation, private or

commercial, that regularly flight on particular route. It includes

almost air transport such as, passenger flights, cargo flights, and

mail flights, while general aviation means chartered flights.

SCA contributes much to the growing rate of the most used (by

passenger) type of civil aviation because general aviation is not

commonly used in Indonesia. By providing the best quality of

services to the customer of

SCA, aviation industry in Indonesia will be very likely to grow much faster near in the future.

However, put aside all of its benefits, there are still lot of lacks and there are still many things that can be developed in aviation industry in Indonesia. One and the most important thing that aviation in Indonesia lacks is it’s poor safety performance. There are so many aviation accident happened in last few years. Aviation accident is an occurrence associated with the operation of an aircraft which takes place between the time any person boards the aircraft with the intention of flight until such time as all such persons have disembarked, in which a person is fatally or seriously injured, the aircraft sustains damage or structural failure, or the aircraft is missing or is completely inaccessible [3]. United State (US) got the highest SCA accident all over the world, but it also has the busiest aviation activity so that its accident rate of SCA is still in the safe category. Compared to Indonesia, whose number 8th in the list of SCA accident all over the world, Indonesia’s SCA accident death rate is almost 10 times higher than in the US, although that US has total flight hours 30 times more than Indonesia’s (See Fig. 1) [4, 5].

Many studies have been done to analyze the cause of SCA accident and most of them conclude that the human and organizational factors play significant roles in the occurrence of civil aviation accidents. Shapell and Wiegmann conducted a research about aviation accident and proved that human error has been implicated in 70 to 80% of all civil and military aviation accidents [6]. Other researchs are done by Vilela and Al Wardi by analysing aviation accident in Brazil and Oman respectively pointed to the crucial areas of human factors and organizational errors that required further exploration to support flight safety in the aviation industry [7,8]. The same result revealed by (Poerwanto & Mauidzoh, 2015) who stated that the cause of aviation accident that happened between 2007 and 2014 in Indonesia were dominated by human factor [9]. From the mentioned studies above, we can conclude that human and organizational factor are the most crucial thing to considerate regarding the root cause of aviation accident.

The literature review above shows that it is nessecary to comprehensively analyze the human and organizational factor of SCA accident to ensure the development of safety measures for SCA companies, prevent fatalities, and reduce economic loses. The model or the method applied during accident analysis determines the result obtained from the analysis [10]. Therefore, an analitycal tool that mostly take human and organizational factor into consideration is required for analysing the SCA accident.

The basic concept and logic of all model that has been used for this modified model is Swiss cheese model of


18

Serious Simulation Game Preliminary Design as

Education Tool for Waste Electrical and Electronic

Equipment Management System in Indonesia

Laksmi Ambarwati




Depok, Indonesia

[email protected]

Romadhani Ardi




Depok, Indonesia

romadhani.ardi @ui.ac.id

Abstract— In a developing country like Indonesia, waste

electrical and electronic equipment (WEEE) management

system is not yet established. Government and the electrical

and electronic equipment (EEE) manufacture still have

minimum knowledge about WEEE. In the other side, serious

simulation game is found to be effective for educating users

through its engaging environment and experiencing the

decision. Serious simulation game as environmental education

tool is no longer a new field of research. However, there are

still few serious simulation game researches that bring waste

management issues, and none focusing on WEEE Management

education for the stakeholder. Thus, the purpose of this

research is to develop a preliminary model for WEEE

Management Serious Simulation Game to educate the

stakeholder about WEEE, why it needs to be managed, and

how it should be managed. The aim of this game is to find the

best scenario that resulting the most volume of WEEE

collection. A multiplayer game infrastructure is implemented

to allow interaction between players. It enables the player to

understand the different result for different decision taken

both in collaborative and competitive mode. The preliminary

model developed in this research will include the conceptual

model of WEEE management system under study, the input,

action and output of the stakeholders, as well as the

information flow between them

Keywords— waste electrical and electronic equipment

(WEEE), serious simulation games, education tool

I. INTRODUCTION

The definition of waste electrical and electronic equipment (WEEE) itself refers to all items of electrical and electronic equipment that have been disposed of by the owner without the intention of reuse [1]. The amount of WEEE globally in 2016 reached 44.7 million metric tons, equivalent to 4500 Eiffel towers, and only 20% of them are documented to be properly recycled [2]. The growth rate itself ranges from 3 to 5 percent per year since 2005 [3]. According to Industry Development Analysis Report 2017 [4]. Indonesia's number of imports in Computer, Electronic and Optical Goods industry increased for US $ 19.2 million (8.5%). This number tends to increase every year. Given that electronic products are not consumable consumer goods, the increase in electronic sales will result in high WEEE as well.

Several components in WEEE classified as hazardous and toxic (B3) waste which can cause health and environmental problems so that special management is needed before disposing it [5]. In Indonesia, the condition of WEEE management is still quite far from ideal, i.e. WEEE is

still grouped into non-specific B3 waste [6] [7]. Moreover, Indonesia has only reached the stage of collecting WEEE, not yet in the management stage [8]. Implementing the collection and management of WEEE in Indonesia, among other things, become challenging due to the attitude of end users itself. They mostly do nothing with their WEEE when it's outdated [9]. In addition, WEEE collection practice is also dominated by the informal sector [9].

The ideal WEEE management system requires cooperation from the government, electronic producers and the community itself [10]. Practices in several countries shows that cooperation and coordination of several stakeholders will result in sustainability in the waste management system [11]. Stakeholders here are actors who have a related issue, whom influenced by the issued, or who are due to their position, have a passive active influence on decision making and implementation related to the issue [11]. That is why education is needed for relevant stakeholders to know the importance of WEEE management, the role of each stakeholders and the relationship between stakeholders. Answering to the need, serious simulation game offer a reliable approach to understanding complex systems by giving players experience about every decision made [12].

Serious simulation game is a game that has educational goals, provides scenario simulations that promote learning in a fun way [13]. Serious simulation game provides an overview of the consequences for each decision making in a risk-free environment while still illustrating the actual system [14].

Considering the condition of WEEE that has not been well managed in Indonesia, the complexity in handling WEEE, and related stakeholders have not been well educated, this research was prepared with the aim of designing a learning media in the form of serious simulation game about WEEE management systems.

This current work is a preliminary research. The conceptual model built in this paper will be used as a basis to conduct a complete design process of the game. Some more comprehensive and beneficial of the serious simulation game as an educational tool are expected then. Nonetheless, the input and outputs of the WEEE managements system in the conceptual models are provided as the outcome of this current work.


19

Service Quality Assessment Of X and Y Generation

Frontliner Using Integration Of Servqual and Kano

Model

Asiyah Nur Mahmudah


University of Indonesia

Jakarta, Indonesia

[email protected]

Teuku Yuri M. Zagloel


University of Indonesia

Jakarta, Indonesia

[email protected]

Abstract—Service quality is a very influential thing to

customer satisfaction. One of the factors that influence service

quality is the front liner's personality who meets directly to

serve the customer. This personality can be influenced by the

age of the front liner or what is currently rampant is called a

generation. This study aims to get the value of service quality

from frontliner of generation X and Y and determine quality

attributes that can improve customer satisfaction. Selected

quality attributes of each generation will be used by companies

to create the same service quality standards. Questionnaires

will be carried out to obtain customer satisfaction ratings on

service quality provided by generation X and Y front liner.

Data is obtained from electricity service provider as a case

study because it has characteristics as a service company and

uses front liners in serving customers. The model was

developed using Servqual integration and kano models which

were added with the front liner X and Y generation factors to

obtain service quality values and attributes that could be used

to increase customer satisfaction.

Keywords—Service Quality, Customer Satisfaction, X and Y

Generation, Servqual, Kano Model

I. INTRODUCTION

Current business competition is forcing many service industries to be able to improve services more efficiently, faster, and more responsive to complaints from customers compared to their competitors. If in the past, service company competition occurred in the type of product offered, this paradigm now has been changed. At present, competition occurs at the speed of service and the updating of the types of services provided by service companies.

Service quality is one of the key success factors that influence an organization being a successful company. Service quality has very high importance in a highly competitive sector (Alnsour et al. 2014) and is the way of working for companies that strive to improve quality continuously towards the processes, products, and services produced [8].

In several researches [6] [15] [21] the implementation of Servqual and Kano integration is used to measure the service quality and to determine the category of products/services attributes based on how good that products/services could satisfy the customer’s needs. In this research, it is expected that Servqual and Kano integration method can be used to close the hole of research in determining service quality which is given by front liner from generation X and Y in a company.

The study is expected to make two major contributions to the literature of the service company's quality. First, it provides an integrated model for investigating perceived

service quality and customer satisfaction in a different generation of frontliner/server in electricity service provider in Banten. Second, give a recommendation to company management from the attributes produced by the integration Servqual, Kano, and X and Y generation factors can be used as innovations in standardizing the service quality desired by the customers..


A. Generation of Human Resources

Human Resources (HR) is one of the important components in an organization, especially in service companies that require HR to be able to service the customers. The quality of good human resources has proven to have a major impact on organization success (Robbins, 2003). In line with [4] statement which states that human resources are the determinants that distinguish positions in a competition. This shows that human resources are very influential in the success of a company. In a company, there are several generations of HR or employees who work at certain times.

A generation is a group of persons whom their year born and life event impact on the same phase of critical development. The life events that experienced by every generation influence the feeling of organization assessment in the job field, therefore, every generation group has its own certain characteristics that make them different from others [10]. According to [18] generation X is a group of people who were born in 1962-1980 while generation Y is a group of people who were born in 1981-2000. There are basic differences in the characteristics/stance between generations X and generation Y toward the job field. The characteristics of generation X are independent and loyal they also have life-work balance and hard worker [1]. Meanwhile, according to [13] the characteristics of generation Y are expert in technology, less tough in facing problems, tend to get something quickly/instant, easy to feel bored, and tend to love freedom in the job field.

B. Service Quality

Service quality is an effort to fulfill the needs and will of the customers as well as the pertinence in delivering a message to equalize customer’s expectation [20]. Quality is a dynamic condition that related to product, service, process, human, and environment that fulfill or beyond the expectation [20]. The qualified service is very important to satisfy customers, besides; it can create a company's profit higher. The more quality that is given by the company will bring more customers' satisfaction [14]. [2] researched that


20

A Causal Modelling Proposal for WEEE

Management System Funding Scheme in Indonesia

Asy’ari Fauzan




Depok, Indonesia

Corresponding author:

[email protected]

Romadhani Ardi




Depok, Indonesia

romadhani.ardi @ui.ac.id

Abstract— Indonesia suffers from the global traffic of

illegal WEEE and as well as the vastly increasing inland

production of WEEE due to the loop hole created in our

national regulations regarding this matter. Several studies

have stated the important role of government in handling the

issue, especially in developing countries such as Indonesia, by

issuing policies related to WEEE management system. Policies

to handle WEEE management system, both in developed and

developing countries, mainly based on EPR principle, but

designing and implementing such principle in developing

countries might be formidable as it should be depended on the

country's capacity and socioeconomic condition, implying that

the financial aspect will be the primary hurdle for the

developing countries when implementing this principle. China

is one of the few developing countries that successfully

replicate this principle into their WEEE management system

and has several similarities with Indonesia, such as the

existence of informal sector, large population, a developing

country. China WEEE processing fund policy uses levies or

subsidies that are set on appropriate number run the system.

In this paper, a causal-loop between the elements in the WEEE

management system is proposed to analyze the possibility of

such policy to be implemented in Indonesia. The causal-loop

diagram of the system is designed to help understand and

visualize the relationship between the elements that formed the

WEEE management system. The causal-loop diagram is

designed in accordance with Indonesia capacity and

socioeconomic in order to ease the implementation in the

future. The funding scheme proposed in this study is divided

into two phases. In the first phase, the fund will be depended

on the government, while in the second phase, the fund will be

shifted from government to the customers.

Keywords— Waste Electrical and Electronic Equipment

(WEEE), System dynamics, EPR, Recycling Fee

I. INTRODUCTION

Electrical and Electronic Equipment (EEE) production is increasing every year globally due to the rapid development of information industry and the accrual of society’s consumption level of EEE. Moreover, the shifting on lifestyle among society also influences the rapid increase of EEE consumption [1], both in the developed and developing countries. The increment development of technology may not only result positive effects that human expect, it also results negative effects. According to [2], increasing level of EEE consumption resulting in the increase of its waste, commonly known as Waste of Electrical and Electronic Equipment (WEEE). [2] stated in that by 2016, the generation of WEEE had grown to 44.7 Million Metric Tons annually. This number is equivalent to almost 4,500 Eiffel Towers and predicted to continuously increasing each year.

According to EU Directive, WEEE a complex mixture of materials and components that because of their hazardous content, and if not properly managed, can cause major environmental and health problems. EEE industry is predicted to consume 100% Indium, In), 72% Ruthenium (Ru), 50% Tin (Sn), 44% Copper (Cu), 34% Silver (Ag), and 22% Mercury (Hg) mined every year [3] which means that these elements are contained in every product the industry produce. Furthermore, this industry also uses several hazardous compounds such as Lead (Pb), Cadmium (Cd), Polychlorinated-biphenyls (PCBs), and Brominated flame retardants. These elements and compounds application in the EEE induce that the waste produce by EEE will also contain the elements and compounds listed above [4].

Referring to the definition of WEEE according to the EU Directive, the improper management of this waste will cause problems, not only for the human’s health, but also the environment. Several researches have been conducted regarding this matter and resulting in the fact that the area surrounding the WEEE informal recycling contained several hazardous pollutants such as dibenzofurans (PCDD/Fs), polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/Fs), and heavy metals [5] and the activities of the informal recycling of WEEE has become the number one source of organics and heavy metal pollutants.

To resolve this, EU published the Directive on Waste Electrical and Electronic Equipment (WEEE Directive) since 2002. The awareness regarding the danger of hazardous substances application is also increasing. This is proven by the increase of the regulation regarding the application of the substances [6]. Furthermore, the rising of better awareness of WEEE management, especially in developed countries such as Japan, Germany, Switzerland, can be seen in the emerging of new regulations published by these countries’ government [7].

In the late 1980, Basel Convention was held with the purpose to design a regulation related to tightening the disposal of toxic waste and its derivatives to reduce their impact on the environment. This convention also aims to restrict the waste movement between countries to avoid waste transboundary movement from developed countries to developing countries [8]. Even though this convention has been established for sometimes, waste exportation toward developing countries remain occurred. Major reasons for this are the lax of law and regulations, lack of environmental and occupational standards, cheap labor, and low awareness of the general public about the WEEE management [9].

The ideal practice to solve the WEEE problems is the application of a circular economy [10]. By applying this practice, WEEE recycling and processing system will not


21

Conceptual Modeling of Safety Culture in Coal

Steam Power Plant Operations and Maintenance

Services in Indonesia

Adithya SudiarnoA, Edwin HermawanB, Sri Gunani PartiwiC


Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember

Surabaya, Indonesia

[email protected], [email protected], [email protected]

Abstract—The safety culture plays a very important role

in shaping the behavior of workers in the operation and

maintenance of the Coal Power Plant. Thus efforts to reduce

work accidents in operation and maintenance must begin by

establishing a good work safety culture. However, the study

of the culture of workplace safety and the influence between

dimensions of the culture of workplace safety is still very rare

in the operation and maintenance of the Coal Fired Power

Plant (PLTU). This research is aimed at knowing empirically

the influence of the dimensions of safety culture on strategies

to improve safety performance. To achieve this goal, first

proposed a model consisting of eight dimensions of safety

culture namely commitment, leadership, responsibility,

competence, engagement & involvement, information &

communication, risk, and organizational learning.

Confirmatory Factor Analysis (CFA) was conducted to

confirm the eight safety culture constructs. This model is then

tested by Structural Equation Modeling (SEM) to identify the

most significant relationship influence. Data was obtained by

distributing questionnaires to 246 workers at PLTU A and

PLTU B in Indonesia using stratified sampling and

measurement methods using a safety culture maturity model.

These findings attempt to help the operation and maintenance

services company management by identifying the significant

influence of dimensions to improve safety performance.

Keywords—Safety Culture, Operation and Maintenance

Services of The Coal Power Plant, Structural Equation

Modelling.

I. INTRODUCTION

One of the objectives of the development of the coal-fired power plant in Indonesia is to ensure the availability of electricity in sufficient quantities, good quality, and reasonable prices in order to improve the welfare and prosperity of the people in a fair and equitable manner and realize sustainable development. In the explanation of Law Number 30 of 2009 concerning Electricity, it is stated that electricity development adheres to a principle, one of which is "security and safety principles", which means that electricity is not only useful but also dangerous, so that electricity supply and utilization of electricity must pay attention installation security, public safety, occupational safety and preservation of environmental functions around the installation. Based on the explanation of the regulation, it can be concluded that occupational safety is defined as a working condition that is free from the risk of accidents or damage or conditions with a relatively very small risk, below a certain level. Therefore, to achieve the

occupational safety goals, especially in the operation and maintenance activities of the coal-fired power plant, it is inseparable from efforts to implement planned, measurable, structured and integrated work safety through the Safety Management System (SMS) as stipulated in Government Regulation Number 50 of 2012 concerning the Application of Occupational Health and Safety Management System (SMK3) or other SMSs such as the Occupational Health and Safety Management System (ISO 45001). SMS is needed to ensure the creation of an occupational safety system in the workplace by involving elements of management, workers, and/or trade unions in order to prevent and reduce workplace accidents and the creation of a comfortable, efficient and productive workplace. The control of accident risk in the workplace must be pursued continuously through both modern safety approaches through a systematic approach and simply by installing safety signs and encouraging workers to care about their work safety behavior. The behavior of workers who care about aspects of work safety plays a very important role in shaping the safety culture. Safety culture is a sub-component of corporate culture, which alludes to an individual, job, and organizational features that affect and influence health and safety [1].

Safety culture can affect safe behavior and can prevent work accidents through two mechanisms, namely: (i) directly through the exploitation of potential failures that suddenly arise (unsafe acts), (ii) indirectly through the exploitation of work climate [2]. Safety culture is multi-dimensional, there is no consensus regarding the dimensions used, depending on the safety culture model used [5]. Based on the object of this study, which refers to the literature study and best practices of the service company of the operation and maintenance coal-fired power plants at PLTU A in East Java and PLTU B in East Nusa Tenggara have 8 (eight) dimensions used in measuring safety culture, namely: commitment, leadership, responsibility, engagement & involvement, risk, competence, information & communication and organizational learning.

Lately, there has been a shift in the way in which occupational safety is measured, from measurements that solely look at the number or level of workplace accidents to measurements that focus on work climate [1]. This consideration is driven by the awareness that the main causes of workplace accidents are from organizational and management factors [3]. Therefore an effort to measure the


22

Conceptual Model of Understanding Procurement

Fraud in Indonesia

Wahyu Seto Syahputra




Depok, Indonesia

[email protected]

Akhmad Hidayatno




Depok, Indonesia

[email protected]

Komarudin




Depok, Indonesia

[email protected]

Abstract - This paper discusses a conceptual model on

understanding procurement fraud in Indonesia, to detect a

non-compliance and fraud in procurement project in

Indonesia. It focuses on a variety of frauds, up to

governmental issues. The problem framework will be made in

the form of a causal diagram to simplify the problem

recognition process. The use of white paper is expected to be

used to determine solutions in better policymaking. Actors

and goals are decided to clear fraud for better Indonesian

economics.

Keywords: Fraud, Indonesia, Procurement, System

Dynamic.

I. INTRODUCTION

''e-Bidding'' is an electronic bidding event (without

awarding commitment) according to defined negotiation

rules (e-Agreement). A buyer and two or more suppliers or

procurement companies take part in this online event. The

committee is the key to holding this event for every

governmental need.

Layanan Pengadaan Secara Elektronik (LPSE) is a

Procurement system of government goods/services that

carried out electronically by utilizing information

technology support. The LPSE system aims to improve

efficiency, effectiveness, quality, and transparency in terms

to implementation of goods and services procurement.

Lembaga Kebijakan Pengadaan Barang/Jasa

Pemerintah (LKPP) is a Non-Ministry Government

Institution which is under and is responsible to the

President of the Republic of Indonesia. LKPP was form

through the Republic of Indonesia Presidential Regulation

Number 106 of 2007 concerning the Government Agency

for Goods / Services Development Policy.

Indonesia’s loan growth to developing the country is

going for its peak-up. Statistical data shows that the peaks

are going from around $7 million to $14 million which

means double the cost. Most of the loan funds are use for

industrial projects. By seeing this kind of curve, the

Indonesian public company feels encouraged to compete

for each other. However, project competition in Indonesia

runs unfairly. So, by using system dynamics analysis, we

wish to solve problems which going on Indonesia.


The procedure for implementation of e-procurement in

Indonesia is divide into several important stages that must

be carried out by all procurement activists from

procurement companies and procurement committees.

Procedures for procurement in Indonesia is [1].

1. Procurement Planning

In this stage, the activities carried out are the

identification of the needs of goods/services needed by

the user as a budget plan. Policy setting and formulation

of the terms of reference are also included in this stage

[1].

2. Election Preparation

The auction committee must submit all available

bidding documents to the available electronic system

(LPSE) and verify to issue the auction code [1].

3. Election Implementation

Procurement committee makes a bundle of documents

in the application, complete with information and

procurement systems. In this stage, an explanation

process with question and answer is also needed,

commonly called aanwijzing in the online procurement

process. If in the process of question and answer

changes occur, a revision will be issued, commonly

refers to the addendum [2].

Checking the qualifications of company documents

also needs to be considered so that the types of

procurement that follows have met the legal

requirements of the auction. After all, documents

prepared by the procurement company are ready, the

offer is submit by uploading to the auction website that

is followed. After entering the time limited to upload,

the next step is the opening of the bidding and

evaluation documents [2].

After the evaluation and determination of the winning

candidates are obtained, the next stage is clarification,

where between the committee and representatives of

the procurement company are brought together to check

the authenticity of the documents. Since the winner is


23

Human Performance Evaluation Framework in the

Complex Control Room

Ghassani Shabrina



Depok, Indonesia

[email protected]

Amalia Suzianti



Depok, Indonesia

[email protected]

Romadhani Ardi



Depok, Indonesia

[email protected]

Abstract— Technology, specifically digitalization, changes

the control room as the heart of the digital industry. Such

room is creating a more complex situation for operators to

process all the information presented into the right decision

while they still need to adapt with the new complex system. The

complexity may affect the operator performance along with the

change of the interaction between the operators and the

systems. The systems include the process control, all the tools

and equipment, the room's design and environment, and all

factors related to jobs and tasks. Therefore, this study presents

a framework to evaluate the operator's performance in the

complex control room. This framework incorporates more

comprehensive human performance dimensions and pay

attention to all of the elements in the systems above as a

contribution to the development of human performance

modeling research. The evaluation will be measured based on

the ergonomics methods and theory and processed by Fuzzy

Set Theory. This framework offers applicable guidance to

evaluate human performance risk that can support the

observer to improve the work design of the operator's job and

also the workplace design.

Keywords—human performance, control room, stress,

fatigue, workload

I. INTRODUCTION

Automation (sensing, detecting, processing information,

making decisions, or controlling any action that can be

carried out by humans but actually carried out by machines)

with high effectiveness and efficiency claims make

automation more developed in various industries in the world

[1]. Automation and CCTV allows all or part of the system

to be monitored and controlled from a main control room, so

all of activities in the control room may significantly affect

the system as a whole. For example, Railway Control Room

is responsible to manage and control all of the traffic

conditions and on the main train line, and power plant main

control room that manage and control all of the machines and

engines in a whole plant system.

There are two types of control rooms, the conventional

control room and the modern control room. Conventional

control rooms rely on analog instruments and control

systems, in contrast to modern control rooms based on digital

equipment [2]. Of course, modern control rooms have a

higher degree of automation compared to conventional

control rooms. All information is collected and analyzed

automatically by computers and all or several complex

decisions and actions made by computers, so that modern

control rooms are referred to as control spaces with high

complexity [3]. Modernization of control room is complex

since many aspects can be influenced such as safety,

operational, engineering, regulatory, and financial

considerations, even small changes to board layouts can

have big consequences to the control technology behind the

boards [4].

A. Human Performance Evaluation in Control Room

Automation changes the way operators interact with

control systems that can reduce operator performance [5]–

[7]. Some studies have shown that workload and operator

performance may increase with the help of modern control

systems [3], [8] but other study said that operator work

becomes more complex which has a negative effect on

operator performance, especially in the emergencies [6]. In

this particular situation, the role of the operator becomes

greater than the control system that plays a role in automatic

decision making [9]. In this situation, a lot of information is

displayed by a control system that can be a burden on the

operator in making the right and fast decision. The operator's

reaction time decreases when there is a lot of data available

because the operator is under high pressure and the amount

of data cannot help or support the operator in making quick

and precise decisions [2]. It can be concluded that

digitalization and automation do not always have the

expected impact, therefore evaluation of human performance

from modern control rooms is needed to see how the impact

of new system design on the operator.

Many studies have been carried out on performance

evaluation in the control room using dimensions of human

performance [3], [10]–[12]. The dimensions of human

performance are related to each other [13], [14]. Performance

is not only related to the type of work but also relates to how

the work is done (work design) and where the work is done

can affect the level of performance [15].

In the complex control room, change includes not only

the role of the changing operator but also the operator's

design work such as division of tasks, workload, position at

work, and others; and designing control room workplaces

such as layouts, displays and more.

Thus, proper measurement of control room operator

performance need to be carried out in an integrated manner,

namely by paying attention to each dimension of human

performance and paying attention to every aspect starting

from the type of work, work design, and workplace itself so

that evaluation will lead to the improvement of all three

aspects overall and human performance can increase and get

closer to the optimal point. Because inappropriate designed

tasks and workplaces cause more errors, higher accident

rates, increased sick time, faulty judgment, and lower

productivity [16].

B. Research Objectives

Previous research in all industries, such as the control room for train traffic, air traffic control rooms, control rooms


24

Integration model of spare parts inventory and

preventive maintenance considering cooling down

and machine dismantling time factors Fachransjah Aliunir



Depok, Indonesia

[email protected]

Teuku Yuri M. Zagloel



Depok, Indonesia

[email protected]

Abstract—In maintenance for most engines, the time for

cooling down and dismantling process occurs very rapidly so

that spare parts must be ready before maintenance or shutdown

activities begin. But for gas turbines, whether it is preventive

maintenance or unplanned shut down, the cooling down and

dismantling process lasts for a few days and even more than 1

week. This unique characteristic has not been considered in

previous studies. An integration model of spare parts inventory

and preventive maintenance is proposed. The proposed model

will consider the time factor of engine cooling down and

dismantling. By using just-in-time delivery, the spare parts

arrive after the completion of engine cooling down and

dismantling period. This model will imitate the situation and

condition of a power generation company in Indonesia. The

discrete-event simulation (DES) model will be built using the

company's operation, maintenance, inventory, and logistics

historical data. By modeling the basic and the proposed model

for discrete-event simulations and also adding stochastic

characteristic in the models, it is interesting to compare how

spare parts ordering time and arrival affect the inventory

pattern of each model.

Keywords—integration, model, spare, parts, maintenance, discrete-

event, simulation, cooling down, dismantling

I. INTRODUCTION

For most companies, spare parts inventory management is a substantial element to focus on, whether in manufacturing or service sectors [1]. Spare parts inventory is certainly different from the production inventory. Spare parts inventory is related to the demand of the engine, while the production inventory is related to the demand of the production process. The variables in the spare parts inventory model are not so much different from the production inventory model, which are usage rate, demand, lead time, ordering costs, storage costs, and cost of materials. In the context of mathematical model, without underestimating the influence of other elements, the usage rate is what distinguishes between the two. Usage rate of production material has a much higher value than the usage rate of spare parts. Usage rate of production is usually in pieces per hour or pieces per day, while the usage rate of spare parts is slower, in pieces per month and even pieces per year.

Spare parts inventory is a research field that is not as popular as compared to production inventories. Various textbooks are discussing production inventory model, ranging from Hadley and Whitin in 1963, then Jacobs and Chase in 1973, to Heizer, Render, and Munson in the 2000s. They all discuss the ideal production inventory model, where demand and lead time are deterministic and the usage rate is constant so that the plot of these parameters is in the form of saw teeth figure known as the renowned Economic Order Quantity (EOQ) model.

The development of knowledge in the field of production inventory has already considered many elements, e.g. engine maintenance or cost of rejecting defective items. The methods are also very diversified, ranging from simple optimization calculation to non-linear, mixed-integer, and stochastic optimization model as conducted by Cheng, Zhou, and Li, 2018 [2]. On the contrary, spare parts inventory model is not available in the main textbooks. Research in this field seems unpopular. Based on searching in several major publishers, it leads to only a few papers that are published in 2014 to 2018.

Even so, the study of the spare parts inventory has been so advanced that it has included various elements, including various costs, utility functions, inventory outages, and replenishment policy [3]. The development of technology and science in the area of statistics, optimization, and simulation are influencing research in this field of study.

Spare parts inventory is very important for facility operations. Spare parts are stored in warehouses to support maintenance activities during equipment failure [4]. A good spare parts management ensures that damaged components in equipment that is failing can be replaced immediately to maintain availability. Maintenance activities are depending on the availability of spare parts in order to reduce engine stop times and associated costs [5]. It is quite clear that maintenance and management of spare parts inventory are closely related and must be analyzed simultaneously [5]. Although the function of spare parts inventory management is well understood by maintenance managers, many companies still face the problem of high inventory which leads to substantial storage cost [4]. Ordering time, the amount to order, and lead time should be planned carefully to minimize storage costs and at the same time to avoid stock-out [6].

The cooling down and dismantling time of the engine has never been taken into account. Maintenance activity is seen in a very general way and is not divided into steps or stages. Maintenance activity is designed to use spare parts immediately so that the parts must be already available prior to or exactly at the start of the maintenance activity, thus causing a certain level of inventory has to be maintained at a certain period of time. And this also applies in the paper that proposes the just-in-time method.

On most engines, e.g. common manufacturing machinery or heavy machinery such as excavators and dozers, the time for cooling down and dismantling is very quick so that replacement parts must be ready prior to or at the start of the maintenance activity. For gas turbines, whether it is preventive maintenance or unplanned shutdown, the cooling down and dismantling process lasts for a few days and even more than 1 week. This unique characteristic has not been considered in previous papers.


25

A Review of Response Surface Methodology

Approach in Supply Chain Management

JanuardiA, Erwin WidodoB,


Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember

Surabaya, Indonesia

[email protected], [email protected]

Abstract—Supply chain management is one of the

important keys to the company. Many supply chain (SC)

optimization research performed deterministically, but if the

model was applied in a probabilistic problem, the optimization

cannot fulfill the objective. In probabilistic supply chain

modeling research using statistic, it's only showed the

relationship of the model to the data. The model couldn't

perform the optimization phase. In this article, response

surface methodology (RSM) is introduced, as a statistical and

mathematical technique to model the data and do the

optimization phase, that's included in a probabilistic problem.

The review reveals that RSM can be an effective method to

model and optimize supply chain problems, even though the

research of RSM in SCM is rarely used. RSM studies in SCM

usually focus on forecasting, supply chain simulation, and

inventory optimization. The used of RSM is quite novel in

SCM modeling and optimization research to develop a supply

chain system.

Keywords— Probabilistic, Response Surface Methodology,

Supply Chain Management

I. INTRODUCTION

Supply chain management (SCM) is one of the fundamental factors in the company's business process. It’s connecting the company with the other actor to support business activity [1]. To support the manufacturing process, the company needs the supplier to gain raw material. To gain some profit, the company needs to find a customer who willing to buy the company's product or service. Then if the company needs to distribute its product to the customer, the company will need a distributor to fill its objective. That's why in the company, the supply chain section is quite important. Many departments in the company are formed to support their supply chain such as procurement, production planning, and inventory control, transportation section, etc. In the supply chain, there is more study that can be discussed. Because it's reviewing the relationship with another actor, so the study such as inventory to fulfill customer demand, routing problems to minimize transportation cost and other related studies. This kind of studies has been included in the modeling.

Supply chain modeling in research always been done by using operation research. The model formulation can be in form of linear programming, integer programming, non-linear programming, and hybrid programming. There is research [2] which tried using mixed integer linear programming to formulate the problem in intermodal container transshipment. But some of them [3] used quadratic programming to solve inventory problem in a dual-channel supply chain. It is happened to be quadratic because

the order quantity function made it non-linear. This kind of approach is also called an exact method. There is more approach with the non-exact method such as using metaheuristic. Such as simulated annealing to solve a capacitated warehouse location problem [4]. This kind of modeling usually is using a deterministic model, which means the model assumes the time function is constant. The problems are modern world solution usually need probabilistic modeling. Real world problems nowadays undefined constrained that affect the optimization. That’s why sometimes, the deterministic model cannot solve the real world problems, it’s because it has many limitations and assumptions.

Probabilistic modeling also regularly used in supply chain research. But the modeling doesn't continue until optimization phase. The probabilistic method usually used in supply chain only to observe the significant, relationship, cluster, and classification of the data. Statistical tools often used by the researcher to model the supply chain management is the analysis of variance, regression, and multivariate statistics such as principal component analysis, partial least square, structural equation modeling, and data clustering. For example, data variance was used to know the significant between inventory cost and order variance [5]. Regression analysis also has been used to model forecasting demand of residential natural gas in an urban area [6]. It's also the same with multivariate research in the supply chain. From the above reason, it's necessary to do probabilistic modeling and optimization in supply chain management. Because, SCM is the fundamental factor in a company, so the decision making should be done fast and precise. Therefore, modeling and optimization using the probabilistic method can be a value added in the research scope.

Response surface methodology (RSM) is a statistical and mathematical technique for modeling and optimizing an experimental design [7]. It is one of state of the art in optimization tools because using only a small amount of experiment, it can model and optimize the data. Usually, this kind of tools was used in a laboratory experiment to find the optimum yield or reaction. But RSM also can be used in management and engineering application such as productivity improvement analysis [8], Simulation optimization of multi-product line [9]. Response surface methods were used to maximize the net present value of cumene extraction plant [10]. Some of the research also have used RSM in supply chain management, but it will be reviewed in the next chapter of this article. RSM as a statistical and mathematical tool is perfect probabilistic modeling to optimize supply chain problem nowadays.


26

A Stock Level Spare Parts by Classification using

ANP - Multi Attribute Spare Tree Analysis: A Case

Study in Plastic Injection Industry

Oksa Angger Dumas



Depok, Indonesia

[email protected]

Zulkarnain



Depok, Indonesia

[email protected]

Abstract— In the maintenance world, line stop is one of many

threats that can give a big loss to the industrial world,

including Plastic Injection industry, which is an industry that

recently has been proposed and developed a lot to substitute

metal industry that relies on the mineral resources. Line stop

occurs due to a partial breakdown of the components while the

spare parts is not readily stock. Therefore, the company needs

more time to get the spare parts from the suppliers, hence it

can make a bigger loss. On the other hand, if the company

stocks more spare parts than needed to anticipate the line stop,

it will face a higher inventory cost. This trade-off can be solved

through an effective inventory system by determining a better

spare parts classification and prioritization, as well as

calculating the optimum stock level. This study aims to propose

an effective spare parts classification method using a

Analytical Network Process and Multi-Criteria Decision

Making (MCDM). An Analytical Network Process (ANP) and

Multi-Attribute Spare Tree Analysis (MASTA) are used as the

MCDM method for spare parts classification, due to its

advantage on possibility to take into account more potential

and intangible factors influencing the spare parts

classification/inventory strategies such as safety objectives,

provisioning characteristics, type of maintenance adopted, and

loss of production. The result of this research is expected to

give a new inventory method as a result of spare parts

classification with combination between ANP and MASTA,

and then setting stock level which depends on the result of

classification that already mentioned before.

Keywords—Spare parts, Classification, Inventory, Multi Criteria

Decision Making, Multi-attribute Spare Tree Analysis

I. INTRODUCTION

In the maintenance sector, line stop is a threat that can

disrupt production process. In some cases, there is an

equipment termination due to failure of equipment while the

spare parts are unavailable on the stocks. The time involved

in acquiring equipment and spare parts from the suppliers

can cause excessive losses because the equipment will stop

during that time [1]. Therefore, a good spare parts inventory

system is needed to prepare a ready-stocked spare parts to

avoid a long line.

The spare parts inventory method uses nearly the same

model for production inventory model, where there is

demand and lead time which is a result of deterministic and

usage rate. However, there are differences between demand

and usage rate that are specifically for spare parts inventory

model. In spare parts inventory model, the demand is from

equipment or maintenance division within the production

line, while for the production inventory model, the demand

is coming from customers. For usage rate, spare parts

inventory model is lower than the production inventory

model.

The use of spare parts in maintenance is very important

but hard to manage because of its random failure [2]. Out of

stock results can be a disaster because the price of the spare

parts is relatively high [3]. Spare parts management is a

special part from inventory management and characterized

as highly erratic, disjointed demand, and with different part

costs [4]. Spare parts inventory is limited by a number of

things including storage space and costs. Therefore, it needs

to be designed optimally for the spare parts so that it can be

applied in the industrial environment.


There are two main approaches to developing a spare

parts decision model [5], namely :

1) Mathematical Model Approach

2) Spare Parts Classification Approach

The first approach concerns with the development of

mathematical models based on linear programming,

dynamic programming, objective programming, simulations

etc. which have some disadvantages, e.g. its complexity so

that the results will be quite abstract or too simple. As for

the second approach, the spare parts classification approach

represents a popular approach in the industrial world. ABC

classification based on the Pareto principle is the most

famous classification. However, this approach is based on

one dimension that does not allow to distinguish all

potential control parameters of various types of goods.

Many researches have been carried out for the development

of this spare parts classification approach. Some examples

are those carried out by Huiskonen [5] and Fuller [6] who

use the classification scheme of six different criteria. Some

works [7] – [10]. regarding the application of multi-attribute

decision making techniques (MADM) for the classification

of parts can also be considered [11]. The application of the

Analytic Hierarchy Process (AHP) methodology for the

classification of spare parts is illustrated in the paper Gajpal

[9] and Sharaf and Helmy [10]. Attributes such as the level

of use of standard reserve characteristics, supply lead time,

spare parts costs are considered in their model. This research

attempt present to present a list of studies in the field of


27

Medical Trainee Scheduling Model Considering

Ergonomic Factors in Teaching Hospital

Tri Novita Sari


Faculty of Industrial Technology


Surabaya, Indonesia

[email protected]

Sri Gunani Partiwi




Surabaya, Indonesia

[email protected]

Budi Santosa




Surabaya, Indonesia

[email protected]

Abstract- The health service industry is required to improve its

performance and service to the society continuously.

Improvements in performance and service can be done by

improving health workers’ performance, one example of which

is improving the performance of medical trainees. Medical

trainees are required to be able to serve patients well, timely and

on target for 24 hours, hence scheduling is needed. Medical

trainees scheduling in this case is to allocate 179 medical trainees

divided into 26 groups, followed by scheduling it into 16

units/clinics/hospital departments for 2 years (96 weeks). Each

hospital department has criteria for mental workload, physical

workload, fatigue level, group capacity and different duration of

time. Therefore, there is a need for scheduling that takes the

whole thing into account. Scheduling in this case is still

conducted by plotting medical trainees manually and has not

considered ergonomic factors (the physical workload, mental

workload and fatigue level) at each hospital department. Medical

trainees scheduling considering ergonomic factors can be

adopted to reduce ergonomic risks and achieve better

performance of medical trainees. In this paper, the authors

propose an integer nonlinear programming which aims to find

optimal scheduling to minimize the deviation in mental

workload, physical workload and fatigue level that ware

experienced by medical trainees in every month. The mental

workload, physical workload and fatigue level were evaluated

using the NASA TLX method, pulse rate recall questionnaire

and Subjective Self Rating Test (SSRT). Optimal scheduling is

needed to reduce the fatigue felt by medical trainees during 96

weeks of clinical clerkship. The results revealed the effectiveness

of the model because the scheduling in each department was

proven to be done in according to the capacity and time

vulnerability based on the regulations and could create a balance

of physical workload, mental workload and fatigue level of

medical trainees on a monthly basis.

Keywords—Medical Trainees Scheduling, Ergonomic Factors,

Integer Non-linear Programming, Workload

I. INTRODUCTION

Health is a human right and one of the elements of welfare

that must be manifested in accordance with the aspirations of

the Indonesian people as referred to in Pancasila and the 1945

Constitution of Republic of Indonesia. Comprehensive,

directed and integrated health development is needed to

achieve the aspirations of Indonesia [6]. In relation to these

health development efforts, the health service industry is

required to continue to improve performance and service to

the society. This improvement can be done by improving the

performance of health workers. It can be said, one

professional worker who determines the quality of health

services at the hospital is a doctor.

Before becoming a doctor, there is an important stage that

must be passed by a doctor after graduating from the Faculty

of Medicine (pre-clinic), the stage is "clinical clerkship". The

clinical clerkship stage is a period of medical education that

is emphasized the application of theoretical theory which had

previously been obtained from the period "Pre-clinic".

Students who conduct clinical clerkship are called Medical

Trainees [8]. During clinical clerkship, medical trainees are

required to be able to provide good service, timely and on

target for 24 hours to patients, therefore medical trainees need

scheduling. Medical trainees scheduling is an activity to

allocate a number of trainees in a particular hospital

department for a certain period. Medical trainees scheduling

is included in a Workforce Scheduling Problem (WSP) [12].

Many researchers have developed WSP, especially in the

manufacturing industry.

Yaoyuenyong and Nathavanij [13] developed WSP on bin

packaging to schedule the minimum number of workers to

perform a set of physical tasks so that their daily energy

capacities did not exceed the limit set. This study considers

ergonomic factors focusing on human aspects such as

physical workload, fatigue level and as well as focusing on

job characteristics such as task’s physicality difficulty and

safety risks. This study uses a mathematic formula and is done

by comparing the heuristics method and the exact algorithm

Azizi et al [1] developed WSP on the manufacturing

industry to ease employee’s boredom and exploit the effect of

rotation intervals on worker’s skill learning and forgetting.

This study considers ergonomic factors focusing on human

aspects such as skill variability, learning and forgetting, and

boredom, as well as focusing on job characteristics as task’s

physicality difficulty and safety risks, and as well as focusing

on physical environment as temperature. This study uses a

mathematic formula and done through the metaheuristics

method.

Setiawan [7] developed WSP on the manufacturing

industry to get maximum profit. This study considers

ergonomic factors focusing on human aspects such as

physical workload, fatigue level, and skill variability,

focusing on job characteristics such as task’s physicality

difficulty and safety risks, and as well as focusing on physical

environments such as temperature and noises. This research

uses mathematic formula (integer programming) and is done

through an exact algorithm.

Belien and Demeulemeester [2] developed medical

trainees scheduling on the service industry (hospital) to

minimize the total schedule cost. This study still does not

consider ergonomic factors and considers only constraints


28

Designing Organizational Persona in Understanding

B2B Environment Using Cluster Analysis

Arsila Chairunnisa



Depok, Indonesia

[email protected]

Amalia Suzianti



Depok, Indonesia

[email protected]

Romadhani Ardi



Depok, Indonesia

[email protected]

Abstract—As customer's buying behavior

rapidly changes over time, user persona is a well-known

approach to gain more understanding on customer

needs. Whether B2B or B2C, both forms are about

creating customer experience that is person-to person.

However, there are several major differences between

supporting a business customer in B2B and consumer

customer in B2C. In B2B environment, multiple people

are using the product within the customer company that

could lead to a lack of customer understanding as whole.

This study aims to adapt the application of user persona

in understanding B2B environment by using cluster

analysis in classifying each organizational persona.

Keywords—persona, design thinking, B2B,

organizational persona

I. INTRODUCTION

Persona is a well-known useful approach for describing

target users [1]. It represents a group of users and is not

based on stereotypical assumption [2]. Persona is not a real

person, but in the form of hypothetical archetypes from real

users [3]. According to measuringu.com [4], in 2016, 70%

of practitioners & researchers have been reported using

personas in defining users and requirements. It helps them to

understand who users are and what they want to accomplish.

In either B2B or B2C environment, both are about

creating customer experience that is person-to person.

However, there are several major differences between

supporting a business customer in B2B and consumer

customer in B2C. The implication in B2B environment is a

lack of understanding of the customer as whole. B2B

environment includes fewer but larger customers, the

company usually sells a large number of products to other

company so each interaction with a customer has more

revenue implication. In consequence, a mistake of customer

understanding and support in B2B environment could lead

to a serious impact on revenue.

As mentioned before that persona is about

hypothetical archetypes from real users, organizational

archetypes has been practically used in understanding users

in a scale of company/organization. Nevertheless, there is

still no standardized approach for organizational persona

construction. Types of components in constructing

organizational archetypes varies from organization size,

type of business, and number of office location, and

organizational persona is said to should be describing any

relevant characteristics of an organization itself such as

objectives, processes, constraints and so on [5]. However,

according to Ortbal, Frazeete, & Mehta (2016), there are

several component of constructed stakeholder personas in

scale of organizations including sector, years in operation,

total revenue, reach, nature of engagement, and payment

practices [6].

Another theory comes from Bob Apollo in 2015, a

founder of Inflexion-Point Strategy Partners (UK-based

B2B sales and marketing performance improvement

company) [7], that traditional dimensions of demographic

segmentation (size, sector, and geography) are an

inadequate basis for identifying the organization. Structural,

behavioral, and situational characteristics are also important

indicators and together, these factors help to define any

organization persona. This study tries to include and merge

some components mentioned into these four characteristic

factors for organizational persona basis.

The object of this study is Indonesia’s B2B food

technology platform who manage local catering vendor and

provide them to company/organization clients. In 2018, this

company’s B2B sales has 43,5% contribution to total sales

and can be said potentially increasing. In this study, survey

is conducted using questionnaire. Furthermore, it tries to use

data collected for cluster analysis as basis in classifying

each organizational persona as it usually use in user

personas.

II. THEORITICAL REVIEW

A. Persona

Persona could be constructed through conducting either

user interviews or use surveys. After determining the right

questionnaire of the survey, survey could be conducted in a

time (Olsen, 2015)[1]. Organizational persona is an

appropriate way to deal with B2B markets for marketing

purposes since a group of people may collaborate to reach

the decision together. It is useful for describing the overall

context in which the other personas operate and allows to

save space by including information that would otherwise

need to be repeated in each of other personas (Pruitt &

Adlin, 2006)[7].

B. Clustering Method

Clustering method or commonly referred to cluster

analysis is a statistical method, specifically a multivariate

technique which classify individuals or objects based on

their characteristic similarity [8]. Cluster analysis can be

used to determine the relationship between several variables

to find existing pattern in a data that may be latent or cannot

be observed by human eyes [9]. Due to its usefulness,

cluster analysis can be used in classifying personas. There


29

Job Rotation Model Considering Ergonomic

Factors in Educational Institutions

Mirsha Ulfatul Haqni




Surabaya, Indonesia

[email protected]

Sri Gunani Partiwi




Surabaya, Indonesia

[email protected]

Budi Santosa




Surabaya, Indonesia

[email protected]

Abstract—Job rotation can be defined as workers who move

from one task to another which are classified based on various

knowledge, skills, and abilities of individual employees. The

current job rotation has not been effective because of not

considering any factors. This study intends to use ergonomic

factors in the development of a job rotation model. This study

was conducted through several stages including identification

of ergonomic factors, data collection, and mathematical model.

The objects observed were ITS who had general functional

positions and structural positions that were occupied by

educational staff within the 35 departments. Data collection by

distributing NASA-TLX and Industrial Fatigue Rating

Committee (IFRC) questionnaire. The mathematical model of

job rotation by considering ergonomic factors that will be

carried out in ITS namely an integer nonlinear programming

using LINGO software to a minimum deviation of employee

performance.

Keywords—Job Rotation, Ergonomic Factors, Integer Non-

linear Programming

I. INTRODUCTION

According to law number 12 of 2012 concerning university, Perguruan Tinggi Negeri Badan Hukum (PTNBH) is a universities established by a government as an autonomous public legal entity [1]. The government increased the number of state universities a legal entity in 2014 including Universitas Padjajaran, Universitas Diponegoro, Universitas Hasanudin and Institut Teknologi Sepuluh Nopember (ITS).

ITS set as PTNBH based on government regulation Number 83 of 2014. In order to support PTNBH then ITS needs to make some changes where one of them is in the structure of human resources that refers to government regulation Number 4 of 2014 Article 25 Point 4 [2]. One of the most important elements for organizations is human resources because human resources will affect the efficiency and effectiveness of the organization in conducting business [3]. Change in the structure of human resource is focused on assignment, guidance, and development of human resource in terms of job rotation. Improper job rotationcan result in decrease employee performance [4]. Job rotation by considering ergonomic factors such as the human aspect, physical environment, and job characteristic enable multi-skilled employees, create workload balance and improve employee performance [5].

Rahayu [6] used integer programming to maximize productivity in the motor assembly line. This study considering ergonomic factor focused on human aspect include variability skill, fatigue, physical workload and boredom, focused on job characteristic and focused in physical environment including noise and temperature.

Azizi [7] used metaheuristic and integer programming to maximize productivity, maximize skill and minimize boredom in manufacturing. Both considered an ergonomic factor in human aspect including variability skill, boredom, learning, and forgetting. This study also focused in job characteristic but not considered the physical environment.

Michalos [8] used a heuristic method to maximize productivity, maximize skill and minimize boredom in the assembly line. This study considered ergonomic factor focused on human aspect including variability skill, fatigue, and physical workload, focused on job characteristic and focused in physical environment including temperature.

Deljoo and Aryanezhad [9] used integer programming to minimize noise and minimize injury spine in manufacturing. Both considered ergonomic factor focused in human aspect including variability skill, focused on job characteristic and focused on physical environment including noise.

Badhury and Radovilsky [10] used a heuristic to minimize boredom and cost in manufacturing. This study considered ergonomic factor focused on human aspect including boredom, focused on job characteristic but not considered the physical environment.

This study aims to create a job rotation model in educational institution considered an ergonomic factor in balancing employee performance. This study focus in human aspect including variability skill, fatigue, physical workload [6] and mental workload, focus in job characteristic aspect [11] and physical aspect including noise and temperature [6].

II. METHODOLOGY

Ergonomic factors in the human aspect that are widely

used, namely physical workload. To fill the research gap this

study will consider mental workload. This study proposes a

new goal and one additional constraint to the mathematical

model from previous research. The mathematical model is

presented below.


30

Social Cognitive Modeling On The Instagram

Towards Health Information

Adithya SudiarnoA, Jesilia Saraswati PutriB,


Faculty of Industrial Technology, Institut Teknologi Sepuluh Nopember

Surabaya, Indonesia

[email protected], [email protected]

Abstract— This present study explored relationships

between millennial and social cognitive on the use of Instagram

for health information access. Millennial also pays attention to

health information besides accessing entertainment on

Instagram. The integration of two models, Protection

Motivation Theory (PMT) and The Unified Theory of

Acceptance Use of Technology (UTAUT) used in this case.

Also, introducing a new variable, credibility. There are several

factors from PMT and UTAUT that related to social cognitive

when using technology. These factors will use as variables for

further investigation. The variable made into eight hypotheses

proposed in this study.

Keywords— Health information, Instagram, Millennial,

PMT, Social Cognitive, UTAUT

I. INTRODUCTION

This digital era introduced with Web 4.0. Web 4.0 is a

mobile web, where all electronic devices can be connected

with other electronic devices and the existence of real-time

technology [6]. Real-time is the level of computer

responsiveness that is felt quite quickly by the user or that

allows the computer to continue to follow several other

external processes, such as changes in weather forecasts [7].

The development of web technology is inseparable from the

role of the human behind it. The high level of education and

the ease of accessing all information made some researchers

encouraged to continue innovating. It is also related to the

millennial generation that is very updated about digital

technology. Millennial know the world where computer

devices and information are easily accessible and have

different expectations about technology, communication and

access to information [2]. Millennial are populations born

from 1981 to 1996 (those aged 22-37 in 2018) and

populations born from 1997 to 2000 and above are part of a

new generation [8].

The millennial generation is a generation familiar with

the internet and social media. The growth of social media

users in Indonesia reached 23 million [1]. Based on the

results of pilot tests conducted in this study, 35.5% of the

110 respondents chose the most attractive Instagram. The

pilot test results are strengthened by data from [9] which

states that Instagram users in Indonesia are 10.44%.

Instagram is a social media based on photo-sharing, where

users can upload and share photos and store them for a long

time [15].

The case of this study is about health information access

to social media use. Beside accessing entertainment,

millennial also considered about health. So, they tried to

access the information what they wanted it. Instagram not

only provides entertainment, but also edutainment. There are

so many styles of health information that Instagram had.

Most of them are infographic which has an image and little

explanation.

The human ability to access information related to

social-cognitive. Social cognitive is the study of changes in

social behavior based on the concept of reciprocal

interaction [23]. Some social cognitive factors related to

human and technology are self-efficacy, self-regulation,

habit strength, past experience, and desired outcomes

(expected outcomes) [23]. Self-efficacy has been identified

as one of the important factors of motivation, influence, and

individual behavior [16]. Self-regulation represents people's

ability to control their choices, feelings, and behavior

through self-monitoring [24]. Habit strength represents

individual behavior patterns and influences current behavior.

Past experience of a user can also show potential

consequences for behavior and so does the impact on the

surrounding environment [Bandura, 2002a]. Whereas

expected outcomes result from one's cognitive when taking

action such as using innovative communication technology.

In this study using the integration of Protection

Motivation Theory (PMT) and The Unified Theory of

Acceptance and Use of Technology (UTAUT) models.

Protection Motivation Theory (PMT) is often used as a

theoretical basis to learn about protective personal behavior

[10]. Originally, PMT has several factors such as perceived

severity, perceived susceptibility, response efficacy, self-

efficacy, and response costs [10]. PMT has successfully

applied in various interpretations and predictions related to

health behavior involving technology in it [10]. The Unified

Theory of Acceptance Use of Technology (UTAUT) is a

model that has the purpose of explaining the user's intent in

using information systems and behavior of the next use [13].

One development of the UTAUT model is to examine the

acceptance of technology in health information systems in

the health industry. The UTAUT variables used are

perceived usefulness, perceived ease of use, behavioral

intention and usage behavior. Whereas, the PMT variables

used are response efficacy and self-efficacy. In this study,

response costs not included because millennial were

considered accustomed to using Instagram and did not

consider the resources used (internet quota and gadget).

While the variables perceived severity and susceptibility not

included because in this study using social media. These two

variables are more suitable for specific health objects, for


31

Inventory Strategy Planning Model with Fuzzy

Analytic Hierarchy Process and Neural Network

Approaches in the Wiring industry

Fauzie Rachman



Depok, Indonesia

[email protected]

Zulkarnain



Depok, Indonesia

[email protected]

Abstract— Inventory control is very important in

company bussiness. Based on data from the Ministry of

Industry, the electricity cable industry is expected to

experience growth of around 10% -15%. And it is predicted

that this increase will continue to grow for the next few

years, given that Indonesia is developing in terms of

infrastructure and industry. To keep good in track, a good

inventory planning is needed so that the goals are achieved

to meet customer needs. Several previous studies on the

predictions of the quantity of future product stocks,

concluded that inventory, both in the form of raw materials,

in-process goods, semi-finished products and finished

products. The main contribution of this research is to make

decision support models by predicting orders from

customers so as to minimize the risk of inventory failure. In

order for inventory management to be more efficiently

assessed according to experts, the opinions of experts.

Therefore, a combination of Fuzzy Analytical Hierarchy

Process (Fuzzy AHP) and Artificial Neural Network (ANN)

is carried out for inventory management.

Keywords— Inventory, prediction, uncertainty, fuzzy AHP,

artificial neural network

I. INTRODUCTION

The cable industry is currently increasing as their role in delivering their products to automotive companies in the form cable for batteries and cable for car lights. The number of cars and commercial vehicles produced worldwide in 2016 was nearly 95 million units. This is a record after 2008 and represents a 4.5% increase in 2015 production [1]. Production of 95 million vehicles involves the work of more than 10.5 million employees directly in making vehicles and the parts that enter them. While in Indonesia as of 2017 the total installed production capacity of cars in Indonesia is 2.2 million units per year. Indonesia not only has a large population (258 million people), but it is also characterized by having a rapidly expanding middle class. Together, these two factors create strong consumer power. Cable companies as one of the vendors to support vehicle production are required to be stable and at the forefront of providing services to demand that continues to exist. In order to control conditions within a company, focused management is called inventory management.

Inventories are needed by companies to anticipate uncertainty in the company's supply chain activities. These uncertainties are common in lead times, rising and falling levels of market demand, and so on. If uncertainty is not well anticipated, the available inventory is very likely to be

stock-out. This will cause the company to suffer losses, both from late delivery of goods to customers, as well as from opportunity loss [2].

In general, there are two approaches to managing inventory. The first approach is an approach that takes delivery based on the market situation. This approach has the advantage of low storage costs, but is likely to increase shipping costs. The second approach is an approach that predicts market demand and determines the amount of goods sent along with the time of delivery. The advantage of this approach is that low shipping costs due to sending in large quantities, but have a weakness in high storage costs [3]. Based on these two approaches, a new paradigm is formed which pays attention to the advantages and disadvantages of both the strategic and tactical approaches. Regarding to the paradigm of strategy and tactics, the strategic approach is an approach by determining the most influential criteria for inventory management performance. In other words, inventory management is carried out internally by the company by taking into account the criteria that most influence the performance of inventory management. The second approach is a tactical approach, an approach that predicts market demand and determines the amount of goods to be made along with the time of delivery. This approach always pays attention to the number of incoming orders and forecasts designed based on historical data [4].

The AHP method uses the value of the nine-point scale done by Saaty. When choosing a value by a decision maker is not based on a definite decision, getting the right values will be very difficult. Although the scale offered by Saaty provides some flexibility, it does not guarantee that decision makers make satisfactory decisions. Decision makers may not be able to describe the difference in judgment when their judgment is equal to certain numerical values. Therefore, Fuzzy AHP can eliminate inaccuracies and uncertainties in the decision-making process. This inaccuracy can arise due to incomplete information or information that cannot be verified. The Fuzzy AHP method serves to minimize the subjectivity of the assessment of experts, so that the results of risk evaluation become more objective and fairer [5].

The purpose of this study is to give suggestion to Wiring Industry in Indonesia on inventory planning issues especially regarding the criterias dan methods that should be considered. The criteria in this study are quantitative and qualitative criteria related to inventory, from the number of these criteria making it multi-criteria decision making (MCDM). In oder inventory management can be


32

Development of Resilience Management Cockpit

Framework to Startup Enterprise in Indonesia

Dimas Prabu Tejonugroho

Departement of Industrial Engineering


Depok, Indonesia

[email protected]

Amalia Suzianti



Depok, Indonesia

[email protected]

Romadhani Ardi



Depok, Indonesia

[email protected]

Abstract— Startup enterprises are key factor of digital

economic in Indonesia. But, running successful business

startup in Indonesia still have to face challenging issues

because the failure rate is quite large. As a result, robust risk

management process has become a critically important tool to

assist the Indonesian startup to be resilient. This research aims

to develops management cockpit tool to mapping and

improving enterprise resilience capability in Indonesian

startup. Four enterprises resilience capabilities such as

adaptability, agility, anticipatory and flexibility are used as

base to profiling resilience condition in startup enterprise to

linked with resilience maturity model.

Keywords— resilience, startup, resilience engineering,

management cockpit, organizational resilience

I. INTRODUCTION

The expansion and advancement of information technology in Indonesia encourage significant growth of internet users with 143 million internet users in 2017 [1]. The growth of internet users promotes the rapid development of startup company in Indonesia as new technology have enabled a wide variety of new market opportunities and new business practices.

A startup is a temporary organization designed to search for repeatable and scalable business model. [2]. Startup company try to enter an existing market or sometimes open a new market with innovative products or services [2]. MIKTI [3] found there are 992 start-up companies in Indonesia. A few of those startups has matured to be most billion-dollar tech company (unicorn) as an example of Go-Jek, Bukalapak, Tokopedia, and Traveloka.

Although developing the startup companies are critical to the economics of nation, building a successful startup company is still be challenge. According to Ghosh [4], 75% of growing startup was failed. Colis [5] concluded that the main issues of managing the startup company is the failure of managing the resource of company so the company didn’t have enough revenue to stabilize the company. CB Insight [6] explained in detail that the main failure reason of building successful startup are lack of capital, lack quality of human resources, facility, poor regulation, and no product-market fit.

While recent studies from National Intelligence Council [7] shows the business environment in the future has become increasingly volatile and turbulent, only then less of half of the non-executive board members surveyed globally believe their companies are adequately prepared for dealing with

crisis situations Survival is now considered a critical aspect of business and being resilient is important for such survival. Looking for the resilience is providing a strategic solution to business that aims to be one step ahead of the unknown.

As in the volatile business environment, many companies are either insufficiently prepared for or gave mismanaged crisis. Yakola [8] discussed many of managers or decision-makers don’t realize they’re facing a crisis. Crisis can be dealt with only when it is known and addressed, so it is important to have an early signal about the condition of a company. This paper aims to develop a basis of resilience capabilities measurement for startup in Indonesia, so it can give early signal or prediction of a potential resilience capabilities in Indonesian startup.


A. Resilience

The study of resilience has been trending nowadays cause people are more aware of the consequences of low probability event with high impact situation. The concept of resilience is introduced by Holling in 1973 to providing framework how the stability of ecosystem works and its response to perturbation [9]. Across the time, many disciplines like psychology, socioecology, psychology, biology, and business also paying attention to this field with their own perspective and definition to preparing and recovering from uncertainties and unpredictability. It made the study of resilience is interdisciplinary and multidimensional depending on perspective. In psychology, resilience is perceived as the positive adaptive capacity of individuals experiencing adverse conditions [10]. Socioecologist view resilience as the resistance and flexibility capacity of a system in order to attain sustainability [10].

In business, resilience can be defined as a measure of company’s ability to rebound from adverse situations or adapt and create new capabilities and opportunities in challenging contexts [11]. Companies that focus only on conserving original structures, processes, business models, or past successes are not guaranteed protection from future or unforeseen threats. Company that can recognize that post disruptive environments are different and require continuous adaptation to keep abreast of changing environments with innovation, development, and growth. Thus, to be truly resilient, companies need to be prepared for adversity by developing their capabilities and capacity to continuously


33

Adoption of Halal Supply Chain in Indonesia: A

Preliminary Insight

Siti Khodijah



Depok, Indonesia

[email protected]

Romadhani Ardi



Depok, Indonesia

[email protected]

Abstract— The large potential of the halal market is estimated

to reach $ 3.081 billion globally in 2022. The development of

the halal market must be supported by the halal industry and

halal supply chain inside. In a report on the Global Islamic

Economic Indicator Score 2017/18, Indonesia ranks 11th

among 15 countries. In general, the administration of halal

certification in Indonesia is limited to the manufacturing

process and not to entire the supply chain. This study aims to

determine the important elements of the halal supply chain as

a preliminary study for the adoption of halal supply chains in

Indonesia. By reviewing the current literature as a

methodology, this study provides an overview of the important

elements of the halal supply chain in Indonesia. Stakeholders

are elements that need to be made a management model design

which are then escorted by a halal supply chain roadmap to

increase the market potential of Indonesia's halal industry.

Keywords—Halal Supply Chain, Indonesia, Literature Review,

Halal market, Halal Industry, Stakeholder

I. INTRODUCTION

Halal is a word used by Arabs and Muslims, refers to

anything that is deemed permissible and lawful according to

religion whereas the opposite of halal is Haram which

translates to "unpermitted" [1]. Halal practice applies to all

aspects and activities of a Muslim, but it's more than just a

religion. Halal is presented with an Islamic way of life that

addresses behavior, speech, clothing, attitudes, and skills.

Haram, in other parts, can be used and placed in Islam, but

the meaning changes depending on what is given, because

the word Haram can also be considered sacred or holy [1].

Halal and Haram are related to food, but nowadays, this has

exceeded the meaning of food consumption and includes

other areas, for example, logistics and supply chains [1] [2]

and the research that has been done indicates that non-

Muslims also use halal products and use halal services [3]

[4].

The concept of halal is increasingly received attention in

various perspectives. From a business perspective, it has

been seen as a potential business strategy that will attract a

wider market consisting of Muslims and non-Muslims who

buy halal products [8] [12]. From a religious perspective,

the request was made based on the religious belief that

Muslims should only consume halal products.

Through Millennial Consumer Insights - Interaction of

Social Data Analysis, it is known that halal is becoming a

concern at this time because it is the top Facebook keyword

and hastag with 5000 keywords and hastags [5]. Halal

concept is an important element in business and trade, and is

a global symbol for assuring quality and lifestyle choices.

This topic regarding Halal which is being loved, of course,

has an impact on the Halal market which then becomes very

promising thing, but most of it has not been utilized [2]. The

Halal market is very promising thing in view of the four

main driving factors for the growth of the first Islamic-based

market that is a large, young and fast-growing global

Muslim demographic. The global Muslim population is

expected to increase from 1.7 billion in 2014 to 2.4 billion

in 2030. The second factor is the large and fast-growing

global Islamic economies. The third factor is Islamic values

increasingly driving lifestyle and business practices. The

fourth factor is Organisation of Islamic Cooperation (OIC)

economies growing halal market development [5]. The large

potential of the halal market is $3,081 billion in 2022 [6].

The development of the halal market must be supported

by the halal industry. The Halal Industry can be transformed

into 10 clusters, namely Manufacturing, Agro-Based,

Biology, Logistics, Research and Development, Hospitality

and Tourism, Financial Services, Human Resources,

Marketing and Promotion, Entrepreneurs and Development

[7]. The halal industry is categorized into two distinct

differences, namely halal products and halal services.

Similarly, halal industry components are divided into three,

namely food and beverages, non-food, and services. For

example, non-food products include pharmaceuticals, health

products, medical equipment, cosmetics, and toiletries.

Meanwhile, logistics, education, training and consulting,

banking and finance, as well as travel and tourism are all

examples of services in the halal industry. The Halal

industry is a very large and rapidly growing market [8].

Halal practice is not only applied during the

manufacturing process. Instead, this practice extends to

entire the supply chain process, namely from the point of

production (where halal is certified) to the point of purchase

of consumers (where halal products are sold). In the supply

chain, logistics plays an important role in installing halal

products at the point of consumption [9]. A product can

easily be lost halal status if contaminated during

transportation and storage before retail [10]. Therefore it is

important to certify logistical operations in accordance with

halal standards to satisfy the final consumer and assurance

the halal status of the product [11] [12]. The foundations of

the halal supply chain are through avoiding direct contact

with something that is haram, the risk of contamination, and

the perception of Muslim consumers [9]. Risk is based on

product characteristics, while perceptions are based on the

flow of Islamic thought, local fatwas (religious rules), and


34

A Conceptual Framework of Reverse Supply Chain

Activities in Process Industries

Muhammad Fadhlun Adzim




Salemba, Jakarta Pusat, Indonesia

[email protected]

Romadhani Ardi




Depok, Indonesia

[email protected]

Amalia Suzianti




Depok, Indonesia

[email protected]

Abstract—Reprocessing by-products, products that

are out of specification and products that are nearing

expiration, are common in the process industry. Based

on these facts, there is an opportunity to improve reverse

supply chain activities in the process industry. However,

research on Reverse Supply Chain in the process

industry is still very limited. Based on previous research,

there are differences between risk associated with

reverse supply chain and forward supply chain

activities. Research that provides a risk framework from

reverse supply chain activities in the process industry is

very limited. To develop this, information about risk

from a whole reverse supply chain activity in the process

industry is needed, and criteria for that risk are also

needed. This information was obtained from previous

studies and discussions with expert practitioners. These

studies are expected to have a positive impact on process

industries. Additionally, it gives a better understanding

about reverse supply chain activities in process

industries to the practitioners.

Keywords— Reverse Supply Chain, Process Industries,

Reverse Logistics, Risk Analysis

I. INTRODUCTION

Commonly the process industry uses only the conventional supply chain (forward supply chain), and the product considered to be fully consumed in the last consumer. This results in a rapid reduction of resources and the possibility of environmental pollution. In fact, sometimes there are several unused product to be returned to the producer [1]. In the other hand reverse logistics or reverse supply chain activities focus on returning the original value of the used product, resulting in economic, environmental and social values [2]. Reverse logistics (RL) is “the process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal [3].

Most of the chemical industry's raw materials are non-renewable and are sourcing globally (both onshore and offshore), making the chemical industry likely to be vulnerable if there is a disruption in the supply chain network [4]. While on the other hand, chemical products that have expired (will be expired) will be in the category of B3 waste (Hazardous & Toxic Materials) [5]. In fact, attention to the impact of industrialization on the environment continues to increase. As a result the pressure of government regulations

and competition has encouraged companies to know their role in sustainability [6]. [7] defined sustainable development as using resources wisely until our future generations needs is not compromised. Therefore, it is important to minimize environmental impacts. The use of resources and income from excessive or unnecessary waste will have a direct impact on the environment. The activities of production, transportation, use and final placement are potential that can adversely affect the environment [8]. One way to improve sustainability is to work with all parties involved in the supply chain, with suppliers redesigning products so that they contain materials that are more environmentally friendly, the company's internal processes so that they use less resources and minimize waste, while also coordinating with customers to minimize product disposal by developing a process to take back and reprocess the product. Depending on the process, reprocessing not only reduces waste but also reduces the use of new resources from the production of new products. Therefore the reduction of waste through the process of reuse in all supply chain activities is important for the direction of sustainable development [5].

Based on previous studies, there are differences between risk on reverse supply chain and forward supply chain. Studies that provides a risk framework from reverse supply chain activities in the process industry is very limited. This study contributes to practitioners by offering them insights to reverse supply chain or reverse logistics activity in the process industry.

The remainder of the paper is organized as follows: section 2 presents an understanding about process industries characteristics and also reuse activities in process industries. Section 3 depicts reverse supply chains’ activities, together with it risk. Section 4 discusses about reverse supply chain activities in process industries. In the last section, concludes the article and suggests directions for future research.

II. PROCESS INDUSTRIES

Process Industries produce products by agitation, separation, and chain chemical reaction. Ink manufacturers, refineries, and petrochemical are examples of process industries [9]. The process industries generate 3,840 billion dollars in sales globally in 2015. This industry is predicted to be estimated at 5,630 billion dollars by 2025. Among the process industries categories include agricultural chemical, inorganic chemical, bulk petrochemicals, organics, plastic resin, synthetic rubber, man-made fibers, specialties [6]. However, even though it is predicted that sales will continue to increase, over the years the chemical process industry globally has faced various challenges including: margins that


35

INITIAL DESIGN OF ELECTRONIC

WASTE MANAGEMENT MODEL IN

INDONESIA BASED ON THE EXTENDED

PRODUCER RESPONSIBILITY CONCEPT

FROM REGULATOR PERSPECTIVE Bernardo Mariano, Romadhani Ardi



Salemba, Indonesia

[email protected]

Abstract— Technological progress and the increasing level

of economic in a country can lead to an increase in

consumption of goods, also applies to consumption of electronic

goods. Increased consumption of electronic goods will also

increase electronic waste that will be produced in the country,

if not accompanied by good management of electronic waste,

electronic waste can pollute the environment and can affect

human health as well. One of the methods to manage electronic

waste is the application of policies based on Extended Producer

Responsibility (EPR), which is a policy that gives product

responsibility to producers as producers of products, starting

from product is produced to End of Life products including

product return processes, recycling processes and the final

disposal process of the product. Many of developed and

developing countries such as Japan, Korea, Taiwan,

Switzerland have implemented the EPR concept for managing

their electronic waste. Indonesia as one of the developing

countries, still does not have electronic waste management and

special regulations concerning the electronic waste. The

Indonesian government still categorizes electronic waste as a

hazardous and toxic material. So this research aims to find and

choose EPR-based electronic waste handling methods that have

been applied in various other countries that are suitable to be

applied in Indonesia. From the results of the analysis, it will be

obtained an EPR-based electronic waste management model

that is most suitable to be applied in the Indonesian state.

Keywords—Electronic Waste Management, Extended

Producer Responsibility

I. INTRODUCTION

The use of electronic equipment in everyday life is inseparable because the use of electronic equipment facilitates various activities or daily activities of every human being. Recent technological developments make the age of use of electronic equipment used increasingly short, for example, is the reduced age of using a personal computer from initially 4.5 years in 1992 to only about 2 years in 2005 and is predicted to continue to decrease [8]. The reduced age of use has the potential to make electronic waste accumulate every year if it is not handled properly. Electronic waste itself is all types of electrical and electronic equipment and all parts that have been disposed of by the owner tend to not be used again [3]. Electronic waste is divided into six categories [3], namely:

• Temperature exchange equipment or commonly called refrigeration and freezing equipment such as refrigerators, air conditioners

• Screens and Monitors such as televisions, monitors, laptops and tablets

• Lighting such as fluorescent lamps, LEDs

• Large equipment such as washing machines, clothes dryers, dishwashers, electric stoves, photocopiers

• Small appliances such as vacuum cleaners, microwaves, calculators, radios, cameras and others

• Communication equipment and IT devices such as cellular phones, routers, printers, calculators and others

The amount of electronic waste from year to year is increasing along with the increase in the economy of each country, according to a report from UNU[3] it is predicted that the amount of electronic waste in 2016 will be 44.7 million metric tons and is predicted to increase and reach 52.2 million metric tons in 2021. Asia became the largest contributor to total electronic waste in 2016 with total electronic waste of 18.2 million metric tons followed by Europe, America, Africa and Oceania.

Improper handling of electronic waste can cause various problems in the environment and human life, this is because electronic waste contains a variety of hazardous materials which if the handling is not appropriate can endanger the environment and human health. Some of the harmful content of electronic waste and its effects are: The reduced age of use and the continued use of electrical and electronic equipment will result in an increasing number of existing electronic waste, if there is no proper handling it will have an impact on the environment and human health.

Research by Kiddee, P[8] states that one way to deal with electronic waste is the Extended Producer Responsibility (EPR). EPR was first introduced in 1990 by Lindhqvist and is defined as a policy that encourages the increase in the total lifetime of a product by giving producers more responsibility from the product to various cycles of a product, especially in the process of returning products, recycling and final disposal from this product disposal from these products [9]. Another definition of EPR is an environmental policy that makes producers also responsible for a product to the post-consumption stage both physically and financially responsible [10].


36

Evaluating the Use of a Posterior Load Carriage Aid

in Grass-Carrying Activities for Cow Farming

Industry

1st Ni Luh Putu Lilis Sinta Setiawati


Bandung Institute of Technology

Bandung, Indonesia

[email protected]

2nd Khoirul Muslim



Bandung, Indonesia

[email protected]

3rd Hardianto Iridiastadi



Bandung, Indonesia

[email protected]

Abstract—Cow farmers in Indonesia perform grass-

carrying activities daily. Farmers use a simple fabric shawl to

carry the load on one shoulder. As such, many cow farmers

complained of the pain in the shoulders, back, and waist. This

experimental study aimed at evaluating the use of a posterior

load carriage aid in grass-carrying activities. Twelve healthy

male participants participated in the study with two

independent variables involving load masses (i.e., 15 kg and 5

kg) and aid types used (i.e., frame backpack and fabric shawl).

Measures such as joint angles, electromyography (EMG),

ratings of perceived discomfort (RPD) questionnaire, and the

activities-specific balance confidence (ABC) scale were

obtained to evaluate the effect of load masses and aid types

used. The results showed that increased load mass associated

with significantly decreasing cranio-horizontal and cranio-

vertebral angle, higher discomfort, and lower level of balance

confidence. While the use of frame backpack exhibited higher

cranio-vertebral angle and a higher level of balance confidence.

However, left shoulder and right waist received higher

discomfort when frame backpack was used because of the

more distributed weight, compared to the use of fabric shawl

which results in uneven discomfort level on the left and right

shoulders and waists. Conversely, the discomfort rating for

right shoulder and back significantly lower when using frame

backpack. In conclusion, forward head posture increased

during grass-carrying activities, especially one with fabric

shawl and heavy load. Furthermore, grass-carrying activities

weighing in 15 kg (i.e., average 22.6% of participants’ body

weight) appeared to be too heavy to maintain the standing

posture for the participants. The overall results indicated that

frame backpack is a potential intervention to reduce the

ergonomic risks associated with activities performed in cow

farming industry.

Keywords— ergonomic intervention, electromyography,

frame backpack, posture, discomfort, balance confidence.

I. INTRODUCTION

Cow farming has been performed among over five million households in Indonesia [1]. Most of the cow farmers are traditional farmers who use minimal technology in raising livestock. Farmers collect grass as livestock feed with basic activities involving cutting the grass at the field and transporting them to the cages. Even the more modern cow farming industry in Indonesia require such activities of collecting the grass manually. Most farmers concerned with grass transportation performed daily with loads ranging from 30 to 45 kg for as many as 5 to 8 times a day, with walking distances as far as five hundred meters or about eight to fifteen minutes walking. Further, some of the farmers using only a fabric shawl to support the load on the back causing concentrated loading on one side of the shoulders (Fig. 1).

Fig. 1. Grass transportation method by cow farmers

A high physically demanding task performed by farmers, such as lifting and carrying heavy objects with awkward postures might be associated with higher risk of musculoskeletal disorders (MSDs) with highest prevalence reported in the lower back, shoulder, and neck [2]. Studies have shown that carrying a load of 20 kg with a frequency of more than twice per day may be related to low back pain [3]. Similarly, a current preliminary study by interviewing twenty farmers in Bandung and Bogor city found that most farmers indicated several symptoms of musculoskeletal pain specifically in the shoulder, back, and waist. Some farmers even suffered from the pain, they had to take painkillers every day. As such, the rise of health issues among the farmers might become serious in the future, which may contribute to the record of morbidity and mortality caused by MSDs in Indonesia [4].

Despite physically demanding activities related to the risk of MSDs among farmers, minimum efforts have been done for this particular population. Previous research has focused largely on identifying the prevalence of MSDs associated with milking activities [2, 5, 6, 7, 8], whereas the riskiest activity of causing MSDs was load carriage. Other research was conducted to develop policies to increase milk production and the quality with less concerned about health risk among the cow farmers [9, 10]. Therefore, to our best knowledge, this study was the first aimed to examine the effect of a simple ergonomics intervention (i.e. the use of frame backpack) during grass transportation among cow farmers in Indonesia. Using focus group discussion and co-creation methodology with farmers in Bandung, preliminary research was conducted to design a frame backpack which may help distribute and balance the load during grass transportation [11, 12] and may reduce energy expenditure compared to other load carrying methods [13]. Practical reasons for choosing a backpack as the intervention compared to other forms of carrying aids was because of the road conditions, farmer habits, and traditional aid used by the


37

IoT Learning for Electrical Engineering Hezron

IoT and Electrical Engineering

Calvin Institute of Technology and

Sekolah Kristen Calvin

Jakarta, Indonesia

[email protected]

Virginia Lalujan


Calvin Institute of Technology

(Institut Teknologi Calvin)

Jakarta, Indonesia

[email protected]

Ghandy


Calvin Institute of Technology

(Institut Teknologi Calvin)

Jakarta, Indonesia

[email protected]

Abstract—The primary aim of this Paper is to outline

results and to summarize research findings on IoT learning

that increase learning opportunities and give more positive

impact to Electrical Engineering education especially on

machines and sensor in physical object communicating with

each other. IoT learning tries to enhance the learning patterns

variety or how the ways people access and manage information

to acquire knowledge by using smart technology which in our

case is a NodeMCU and Arduino. The successful experiences

build confidence to open wider subject of innovations in

education 4.0 as it is discussed in the paper. It is expected that

the current results motivate and support the running

electronics and telematics education to be optimized in

reliability and effectiveness by using more structure and direct

approach in digital learning technology.

Keywords—Internet of Things (IoT), Electrical Engineering,

Education 4.0

I. INTRODUCTION

IoT play an important role in electrical engineering education to promote the fourth industrial revolution. Sometime people name it as Industry 4.0 [1]. Things or devices that can be connected to the Internet which is enable devices to gather useful information and to share information to the cloud. An autonomous system interpretation, forecasting and decision-making can be made by artificial intelligence using machine learning [2].

IoT is introduced into education to improve learning experience by real-time student response and instantaneous feedback analysis capturing every single detail from all the students. It is possible because every student have their own smartphone or tablet or laptop. This solution is cheaper than to invest smartboard in every classroom. Learning is not limited by classroom walls so it can be outdoors activity or home activity as well. Students can bring their device to anywhere and anytime to study and collaborate. The devices connect well as long as there is internet or intranet available. Learner can be more active with personalized learning and lecturer can give students individual attention by support of IoT [3][4].

A. Technologies behind IoT

The Internet is the reason of the existing of IoT. It is connecting people together and share information. Learning is never out of resources provided by Google, YouTube, open courses, many other websites and eBooks. IoT is connecting every device to the Internet, collecting data from sensors and put the data in the cloud. Cloud is more than storage, it is providing computing power. Artificial intelligence is used in the cloud to provide data analytics. It can analyze every data transmitted in real-time. With IoT, information will be able to be used to predict problems and to prevent them if possible. Industry use IoT to take benefit of the systems efficiency and effectively of decision support system since many things can be predict [5] [6].

B. Industry 4.0 and 5G

Smart factory try to simplify the operations by digital tranformation. Devices, sensors, and controls share information with one another with cyber-physical systems and calculation algorithms for big data [7]. The companies are able to quickly adapt their products and services. Mass production industry transform into a characterized industry. Products or services can be delivered to the right place for the right price with a higher level of sophistication [8]. For example, Uber or Grab change transportation service into a different level of customer oriented satisfaction by using IoT technology [9].

The fifth generation of telecomunication will provide mobile wireless unified framework. IoT devices can transmit 1000x times more data than 4G connection with less network latency and increases data rate [10]. Engineering education is not an exception to digital transformation. Engineering education system with sufficient programming skills is critical for developing digital solutions and digitization processes. It is important to strengthen ICT skills for a well-developed digital infrastructure. The system is vulnerable without capable engineers [11].

C. Benefits of IoT

Once it is running, the system run completely by artificial intelligence. The technology bring reduction in time, cost and energy usage[12]. Integrated systems consist of systems which carry out measuring, modeling, and managing teaching and learning performance. Innovation is the key to enables rapid development and rapid prototyping. Learner-centered and self-directed learning is required infrastructure which enable knowledge share by practice orientations rather than through the orthodox learning. Lecture is focusing on experience and technology which enable student to learn from doing rather than memorizing [13] [14].

There are available apps and devices that provide access, linked up resources that students can use to learn, not just at their own pace, but wherever they have an internet connection. They also provide much greater opportunity for collaboration, not just between students on the same campus but anywhere in the world [15]. At the same time, the data collected by these apps can be used to evaluate the progress of individual students. Personalized learning experiences is made based on individual performance. It gives advanced student opportunities to get more challenging tasks while those struggling can receive assistance. IoT connects clients and Cloud computing resources. The server virtualization utilize large physical resource to simulate many virtual machine to serve the need of computing power to store IoT data, big data analitics and research facilities [16].


38

Crimes Prediction Using Spatio-Temporal Data and

Kernel Density Estimation

Vinnia Kemala Putri

Machine Learning and Computer Vision Lab


Depok, Indonesia

[email protected]

Felix Indra Kurniadi

School of Engineering and Technology

Tanri Abeng University

Jakarta, Indonesia

[email protected]

Abstract— This study presents a method to predict crimes

by using multiple data sources i.e. spatio-temporal crime

dataset and zoning district dataset. The contribution of this

study lies in the use of Kernel Density Estimation (KDE) and

zoning district dataset to address the issue of crimes prediction.

The experiments were performed by training Gradient

Boosting Machine (GBM) as a classifier on some subset of

features. The best result was achieved by using all features

including KDE with smoothing and zoning district feature,

namely with multiclass logarithmic loss 2.356104 on validation

set and 2.35443 on test set.

Keywords— crimes prediction, density estimation, spatial

data mining, data integration.

I. INTRODUCTION

A security issue is a common issue in the community. It is important to understand the pattern in crime activities in order to prevent before it takes place. In this area of Big Data and the availability of fast and efficient machine learning algorithms to analyze data, it is possible to recognize and predict the pattern in crime activities.

Hot-spot mapping is a conventional method that is often used in predicting, analyzing and visualizing the distribution of crime across space and time. Kernel Density Estimation (KDE) is a standard method most frequently used to estimate the density of the crime [1]. However, this method has the disadvantages that care less about the landscape of the area when analyzing patterns of crime.

Several solutions have been offered to solve the problem of crime prediction. Almanie T., et. al [2] conducted a study on the relationship type of crime with demographic data. Bogomolov A., et. al [3] proposed a solution by dividing London area into 124,119 cells and each cell has demographic data taken every hour based on the activities of the mobile network.

Several other works have been proposed to solve the crimes prediction problem in San Francisco. C. Hale and F. Liu employed a mixture of Gaussian model and logistic regression [11]. Y. Abouelnage [12] implemented several different machine learning algorithms. V. Mishra [13] suggested the use of XGBoost for crime categories classification.

Besides using the spatial and demographic data, crime prediction task had also been done by utilizing information obtained from social media. M. S. Gerber [4] added topics distribution derived from Twitter data on conventional method KDE. Other than using Twitter sentiment analysis

and KDE, X. Chen, Y. Cho and S.Y. Jang [5] also added features based on weather conditions.

In this study, spatio-temporal crime dataset and zoning dataset is used to analyze pattern of crime incidents in the city of San Francisco. The purpose of this study is to predict the type of crime that is most likely to occur given the time and location of the incident.

II. DATASET EXPLORATION

The dataset used in this study is taken from Kaggle SF Crime Classification public dataset [6]. The original dataset is publicly accessible at SF Open Data [7]. This dataset has information about 1,762,311 crimes that occurred in San Francisco from 2003 to 2015. Every crime is labeled each one of the 39 categories of crimes.

A. Overview

The dataset is divided into train set (878,049 observations) and the test set (884,262 observations). Data with odd-numbered weeks as train set while the even-numbered weeks as a test set. The dataset consisted of 9 columns as features are:

• Dates – timestamp of the crime incident.

• Category – category of the crime incident, only available in train set. This is the target variable that will be predicted.

• Descript – detailed description of the crime incident. Only available in train set.

• DayOfWeek – the day of the week.

• PdDistrict – name of the Police Department District.

• Resolution – how the crime incident was resolved. Only available in train set.

• Address – the approximate street address of the crime incident.

• X – Longitude

• Y – Latitude

B. Dataset Exploration

To get more insight about San Francisco dataset, it is necessary to explore the dataset deeper. The crime dataset contains of 39 categories of crimes. The most common crime is LARCENY/THEFT.


39

Lower Back Pain Classification Using Machine

Learning

Mutia A. Paramesti

Department of Biomedical Engineering

Institut Teknologi Bandung

Bandung, Indonesia

Hugi R. Munggaran



Bandung, Indonesia

Indria Herman

Department of Mechanical Engineering


Bandung, Indonesia

Aisyah F. Prawiningrum



Bandung, Indonesia

Suksmandhira Harimurti



Bandung, Indonesia

Isa Anshori



Bandung, Indonesia

[email protected]

Akhmad D.H. Syababa



Bandung, Indonesia

Widyawardana Adiprawita



Bandung, Indonesia

Abstract—Most of old people usually suffer from a lower

back pain. The main problem of this pain is the long recovery

time. Some patients may be fully recovered from lower back

pain for even years. Therefore, a preventive action is needed to

be developed to prevent the lower back pain gets worsening.

This paper presents a comparative study of lower back pain

classification method using machine learning technique. The

classification is performed using several algorithms. Moreover,

a performance tuning using Grid Search method is also

conducted. The results show that K-Nearest Neighbor

algorithms provide the best classification accuracy as high as

87.2%. However, after tuning, the best classification accuracy

as high as 86.7% obtained by using logistic regression

classifier.

Keywords—machine learning, lower back pain,

hyperparameter, grid search

I. INTRODUCTION

The lumbar spine or lower back is a complex body part

structure interconnecting bones, joints, ligaments, discs,

muscles, as well as nerves. All work together to provide

support, strength, and movement flexibility of human body.

With its crucial function to support weight and everyday

movement, this complex structure is very prone to injury or

pain [1]. Lower back pain (LBP) is basically a pain which is

resulted from a long-term muscle pressure or stiffness

concentrated below the coastal edge and the painful feeling

typically holds on for 12 weeks or more [2]. About 70-85%

of people suffer from such back pain disorder [3].

Moreover, around 82% of non-recent onset patients

experience the pain for even 1 year after the treatment [4].

Additionally, even though not having any history of lower

back pain, many patients suffered from this disorder spent

months or years healing from it. Hence, it is necessary to

build a preventive action of this LBP. In this paper, a

classification methodology of chronic LBP disorder using

machine learning technique is proposed. The dataset is a

collection of physical lumbar spine data obtained from UCI

Machine Learning Repository [1]. This dataset contains

various angles of 310 subject’s lumbar spine which have

been labeled as abnormal or normal. By classifying the

abnormal/normal degree of lumbar spine, hopefully, people

can know how close they are to abnormality threshold. If

this threshold can be modeled, LBP can be prevented.

To evaluate and obtain the most optimum and suitable machine learning algorithm for LBP case, the classification is conducted using several numbers of algorithms, including Gaussian naive bias, support vector machine, extreme gradient booster, logistic regression classifier, random forest classifier, K-nearest neighbor (KNN), KNN with principal component analysis, and KNN with linear discriminant analysis. Moreover, to further improve the performance, a hyperparameter tuning using Grid Search method is also applied to the algorithms. The performance of all algorithms, without and with hyperparameter tuning, are measured and compared by its classification accuracy.

II. METHODS

The overall methods and steps in this study are shown in

Fig. 1. To obtain the best classification result, we perform a

data cleaning and formatting before further processing the

dataset. As explained previously, the performance is

measured by the accuracy of the classification result. The

following subsections provide a more detailed explanation

of each steps.

Fig 1. Block diagram of classification process using machine learning

A. Importing Dataset

We obtain the lumbar spine dataset from UCI Machine

Learning Repository [1]. It consists of 7 different columns

and contains 310 data in total. The detail process of building

the algorithm to classify the abnormality of lower back pain

is explained in the next subsections.


40

Preliminary Study on Machine Learning

Application for Parkinson’s Disease Diagnosis

Achmad Habibie Thias



Bandung, Indonesia

Navila Akhsanil Fitri



Bandung, Indonesia




Bandung, Indonesia

Isca Amanda



Bandung, Indonesia

Raih Rona Althof



Bandung, Indonesia

Isa Anshori



Bandung, Indonesia

[email protected]

Jessika



Bandung, Indonesia




Bandung, Indonesia

Abstract—Early detection for Parkinson’s Disease (PD) can

be realized by investigating the speech abnormalities of the

patient. Utilizing machine learning approach, PD can be well

diagnosed by investigating its speech features. Oxford

Parkinson’s Disease (OPD) dataset, containing pieces of PD

patients’ speech and normal speech was used in this study. The

investigated algorithms that were tested are Support Vector

Machine, K-Nearest Neighbor, Linear Discriminant Analysis,

Gradient Boost, Multi-layer Perceptron, and Decision Tree.

The performance evaluation of all these methods is based on

accuracy, precision, recall, and F1 score. Based on the

evaluation, the most suitable algorithm for PD case is Multi-

layer Perceptron with the accuracy of 95.92% without data

scaling.

Keywords—parkinson’s disease, speech analysis, machine

learning

I. INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder that affects predominantly dopamine-producing neurons in the central nervous system [1]. Dysfunction in the basal ganglia circuits have the symptoms of being impaired in speech fluency [2]. Parkinson’s patient may differ in vocal intensity levels and higher pitch as well as experiences severe vocal degradation or inability to produce sustained phonations, tremor, hoarseness [3]. Therefore, PD can be observed or diagnosed with the voice of the suspect.

Currently, there is no specific test exists to diagnose Parkinson's disease. Currently, research on the development of decision support tools rely on algorithms aiming to differentiate healthy controls from people with Parkinson’s have been conducted [4]. In this study, machine learning approach is investigated to diagnose Parkinson’s Disease using a dataset of various speech features (a non-invasive characteristic tool) from the University of Oxford. Some machine learning algorithms used in this work are Support Vector Machine, Linear Discriminant Analysis, K-Nearest Neighbors, Decision Tree, Naive Bayes, Gradient Boost, and Multiple Layer Perceptron. This work aims to gain knowledge of which machine learning model with given features of a patient’s speech can give at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9 to predict Parkinson’s Disease.

II. METHODS

A. Dataset

The speech dataset used in this study is taken from Oxford Parkinson’s Diseases (OPD) Detection Dataset. The dataset was created by Max Little from Oxford University, in collaboration with the National Centre for Voice and Speech in Colorado, who recorded the speech signals. This study published the feature extraction methods for general voice disorders.

The dataset is composed of a range of voice measurements taken from 31 people, with 23 people having a Parkinson's disease (PD). The dataset collected in this study contains multiple voice samples per subject, such as sustained vowels, numbers, words, and short sentences. Each column in the table has a particular voice measure and each row corresponds to one of 195 voices recorded from these individuals ("name" column). The data was taken to discriminate healthy people from those with PD. According to "status" column, it is set to 0 for healthy and 1 for PD.

The dataset used in this project has many features that are owned for each data. But the main weakness in this dataset is the amount of data that is lacking with a number of subjects that are only 31 people. All 23 features that used in this study may not necessarily have a significant contribution to the making of the classification model. In addition, the balance in the dataset is also not fulfilled. With the 31 subjects, only 8 people were not Parkinson's sufferers. Dataset weaknesses may affect the accuracy of the resulting model, regardless of what algorithm is used.

B. Features Extraction

One of the important processes in machine learning is the feature extraction process from data. These features will be the input of an algorithm to produce a particular classification model. In other words, a new data (test data) will be classified into certain classes with features from predetermined data. In this case, voice samples are extracted into 22 features. Voice feature extraction uses the Multidimensional Voice Program (MDVP). This program provides several menus and functions, so that feature extraction can be obtained.


41

On the Performance Similarity Between Exponential

Moving Average and Discrete Linear Kalman Filter

Muhammad Fikri, Samiadji Herdjunanto, Adha Cahyadi Department of Electrical Engineering and Information Technology

Faculty of Engineering, Universitas Gadjah Mada

Yogyakarta, Indonesia

[email protected]

Abstract—Raw signal from sensor is generally corrupted by noise and other uncertainties. To suppress the noise, a filtering mechanism is required. Exponential Moving Average filter (EMA) serves as a powerful yet exceptionally simple filter. However, selecting so-called Alpha parameter in EMA is not a straightforward task. Extremely large Alpha value lead to more noise in the signal and small Alpha value resulted in sluggish convergence to the true value. In many cases Alpha parameter is selected arbitrarily and then opted for the best performance, resulting in numerous trials and errors. This paper is aimed to present an insight to select Alpha parameter, that is by implementing Kalman Gain from Kalman Filter structure as Alpha parameter and simultaneously highlight the similarity between Discrete Linear Kalman filter and Exponential Moving Average filter through some mathematical manipulations. To demonstrate the filter performance, altitude data from BMP280 barometric sensor is filtered. The results show that the EMA with Kalman Gain is capable to converge to the true altitude value and for some reasons, EMA with Kalman Gain resembles Kalman Filter performance in this particular scenario.

Keywords—Alpha parameter, exponential moving average, kalman filter, kalman gain, filtering and smoothing

I. INTRODUCTION

Measurements data from various sensor is generally cor-

rupted by random noise. In order to extract the most reliable

information from the sensor, it often requires a handfuls of

preprocessing stage. One of which is filtering. Loosely speak-

ing, filtering is the process of suppressing unwanted signal

that affecting the measurement from a particular signal source.

Filtering can be done in time domain or in the frequency

domain of the original signal. This paper focuses on the

filtering of discrete time domain problem. In the time domain

based filtering, several filters have been implemented such as

Moving Average filter [1], Savitzky-Golay filter [2] [3],

Exponential Moving Average Filter [4], and Ramer-Douglas-

Peucker Algorithm, it has been applied in robotics to perform

simplification and de-noising range data from spinning laser

rangefinder [5], in this field such a algorithm is known as

split-and-merge algorithm and is attributed to Duda and Hart.

Among all of those filters, Exponential Moving Average

(EMA) is really attractive for its simplicity but still perform

superbly on some metrics such as Signal to Noise Ratio (SNR)

and Statistical Evaluation compared to other filters [4].

However, in EMA selecting free parameter called α is

sometimes can be troublesome and time consuming. Since

EMA is mainly used in forex and stock trading as an indicator

[6] - [8], the free parameter α is closely related to N days

or how many days should all the data be corporated to EMA

structure to give a fruitful result. There is no special consensus

to select α as its value is based on designer objective for

trading. However, for filtering scenario one of many objectives

to obtain meaningful result is minimizing the so-called Mean

Square Error (MSE) [9]. This can be achieved by employing

the widely used Discrete Linear Kalman Filter algorithm [10]

[11] to estimate the underlying information buried from the

original signal source.

This paper highlights the similarity between Kalman Filter

and EMA as well as it gives an insight to select α parameter

value in EMA based on Kalman Gain from Kalman Filter

structure. Hopefully, it achieves the optimality of the Kalman

Filter yet still maintains the simplicity of EMA in terms of

filtering noisy signal source such as sensor.

II. FILTERING METHODS

In this section, both filtering methods are discussed briefly

to give an overview and characteristics for each filter. The

filter structure will be discussed as well to give the reader a

rough idea how the filtering mechanism works.

A. Exponential Moving Average Filter

Exponential Moving Average filter (EMA) is belong to a

first-order infinite impulse response filter that has properties

equivalent to low pass filter. Unlike Simple Moving Average

(SMA), EMA is much more efficient and does not need any

buffer to save the previous sample [12].

In the EMA structure not all previous sample datum is taken

into account, instead the most recent sample gets the largest

portion and all the previous datum will decays in exponential

fashion but never actually reach zero.

EMA has relatively simple recursive structure [13] as in:

l1 = Y1 (1)

for 𝑘 > 1, lk = αYk + (1 − α)lk−1 (2)

where lk is the estimate of the expected value of filter output

at time k, Yk is the observation made at sample time period

k which in this case is the unfiltered signal from BMP280

Barometric sensor. α parameter represents the forgetting factor


42

Internet Of Things-Based Processes Improvement

Of Indonesian Hospital

Egi Aulia Mahendra



Depok, Indonesia

[email protected]

M. Dachyar



Depok, Indonesia

[email protected]

Farizal



Depok, Indonesia

[email protected]

Abstract— Indonesian’s population is one of the highest in

the world and create a potential market in the service industry

especially healthcare. By the end of 2019, 85% of world

business plans will implement Internet of Things (IoT).

Implementation of IoT was driven by business innovation and

business efficiency. This paper aims to establish a framework

to select healthcare practices supported by internet of things

especially in hospital, in order to improve the overall quality of

healthcare processes based on quality of services variables and

quantitative constraints faced by the hospital. Six experts’

opinions are used to identify the relationship and select the

factors and sub factors that support the integration of Internet

of Things in Indonesian hospital. Analytic Network Process

(ANP) method is used to obtain the decision priority of

healthcare practices supported by the implementation of

Internet of Things. Zero-One Goal Programming (ZOGP)

method is used to choose the optimal number of IT’s

employees, decision priority of ANP, procurement cost,

installation cost, training cost, maintenance cost which

healthcare practices will be implemented based on constraints.

The case study was conducted to private and public hospitals

in Jakarta.

Keywords— IoT, healthcare industry, hospital, analytic

network process, zero-one goal programming

I. INTRODUCTION

Indonesian’s population has an average annual increase of more than 1%. By 2035 there will be more than 305 million people living in Indonesia [1]. This relatively high number of population attracts service sector especially in healthcare industry. In 2018 there are 2776 hospitals all over Indonesia, where more than 63% of them are private hospitals. Once again this shows how much potential the healthcare industry in Indonesia has [2].

Indonesian’s population has an average annual increase of more than 1%. By 2035 there will be more than 305 million people living in Indonesia [1]. This relatively high number of population attracts service sector especially in healthcare industry. In 2018 there are 2776 hospitals all over Indonesia, where more than 63% of them are private hospitals. Once again this shows how much potential the healthcare industry in Indonesia has [2].

By the end of 2019 85% of business plans will implement IoT, this plan was driven by two main objective: Business innovation and Business efficiency. Report showed benefits of implementing IoT far exceeds the expectation, these will drive the business world to massively adopt IoT in 2019 [6].

About 60% of healthcare organizations in the world have adopted IoT, 80% of them clearly see the benefits of such adoption and 73% of them stated that one of the biggest benefit is cost saving [6]. Prior studies have discussed

healthcare practices that can be implemented in hospital with the support of Internet of Things [7]–[12] and the aspects of quality that should be integrated in Internet of Things [13]. In this paper, we used the quality aspects mainly discussed in ISO/IEC 25010:2011 as a consideration to select healthcare practices that should be implemented in Indonesian hospital. ANP method is utilized to calculate the priority weight of healthcare practices. ZOGP method is incorporated to consider the constraints in the selection of healthcare practices.


A. Healthcare Service Quality

Prior studies have concluded that quality service is one

of the most important factors to be considered in order to

improve patients’ satisfaction. Healthcare processes

improvement plays a significant role in improving and

maintaining the desired level of quality service perceived by

patients [14], [15].

B. IoT Implementation in Healthcare Industry

Internet of Things (IoT) is an ecosystem that integrates hardware, devices, physical objects, software, and animals or humans in a specific network that enabled them to interact, communicate, record, obtain and share the data [16].

Technology will play a significant role in monitoring

patients in hospital and their home. Remote monitoring will

offer significant benefits such as healthcare quality

improvement and cost savings by identifying hazardous

illnesses. Currently, the cost of healthcare is relatively high,

since its mandatory for most of the patients to stay at the

hospital for the entirety of the medication and healthcare

process. With technology that can monitor patients remotely,

we can easily addressed that problem. IoT technology

collects real data in real-time and send those data to

healthcare providers, thus reducing the healthcare cost and

enabling early detection of disease [17].

C. Analytic Network Process Method (ANP)

ANP method is a method utilized to solve decision-

making problems without assuming the inter-dependence

and inter-relation of factors within a different level or the

same level of hierarchy [18]. To obtain the priority of each

alternative in the decision-making model, pairwise

comparison was used. Pairwise comparison matrices are

constructed by comparing a pair of elements in regards to a

specific component. ANP is used to generate the relative

priority weight of healthcare practices supported by the

implementation of Internet of Things (IoT).


43

Techno-Economic Analysis of Narrowband IoT

(NB-IoT) Deployment for Smart Metering

Amriane Hidayati

Regulation and Management of Telecommunication,

Telkom University

Bandung, Indonesia

[email protected]

university.ac.id

Muhamad Reza

Dept of Electrical Engineering

Telkom University

Bandung, Indonesia

muhamad.reza@telkom

university.ac.id

Nachwan Mufti Adriansyah

Dept of Electrical Engineering

Telkom University

Bandung, Indonesia

nachwanma@telkom

university.ac.id

Muhammad Imam Nashiruddin

Postgraduate Program in

Management, University of Prof.Dr. Moestopo (Beragama)

Jakarta, Indonesia

imam.nashiruddin@dsn.

moestopo.ac.id

Abstract— The Internet of things (IoT) wireless network

are evolving to help meet the needs of a wide variety of

connected devices. 3GPP has introduced a narrowband system

based on Long Term Evolution (LTE) named Narrowband IoT

(NB-IoT). It provides low-cost, wide coverage, long battery life,

and support massive devices. The smart meter has become one

of the main element in smart grids that potential to use NB-IoT

technology and categorized into massive IoT because of its

characteristics requirements. This paper aims to provide a

techno-economic analysis of NB-IoT deployment for smart

metering. The analysis results show that smart metering

deployment will be feasible if there are consumers involvement

considered. In addition, based on sensitivity analysis results,

material costs become the most critical element to bring

successful deployment. Its variations and slight changes have a

significant impact on the overall Net Present Value (NPV).

Keywords— Internet of Things, Narrowband, NB-IoT, Smart

Metering, Cost-Benefit Analysis, Techno-Economic Analysis

I. INTRODUCTION

Narrowband IoT (NB-IoT) introduced by 3GPP and standardized as part of 3GPP Release 13 is a radio access technology solution to support connectivity millions of devices spread over large geographic areas while minimizing power consumptions and batteries replacement of the devices. It was designed to enhance existing Global System for Mobile Communications (GSM) and Long-Term Evolution (LTE) networks for better serving IoT use cases regarding coverage extension, User Equipment (UE) complexity reduction, long battery life, and support backward compatibility [1]. Since NB-IoT design is based on existing LTE functionalities, it is possible to share resources without coexistence issues. For the site with compatible equipment, NB-IoT can be activated by just upgrading the software. It allows for a low-cost and fast deployment of NB-IoT using existing infrastructure. However, older stuff may not be able to support both LTE and NB-IoT simultaneously, and a hardware upgrade is required [2]. It considers that single Radio Access Network (RAN) could support GSM, UMTS, LTE (GUL) with NB-IoT co-deployment. Therefore, the capacity of existing RAN assets must be evaluated to meet the requirements.

A smart meter regarding electricity defined as a digital electronic device that collects information on power. A smart meter is an element of the smart grid in the consumer's side. One of the significant innovations from traditional meter devices is the bidirectional communication link between utility and consumers. These allow understanding spending habits, improving network efficiency, and contributing to electricity saving. By using smart meters, consumption data can be managed, and any impact on the network can be

monitored in real time [3]. The national power utility company in Indonesia referred to as PLN is a state-owned company that conducts electricity supply business that will use in this research as a party in-charge for smart meter deployment using NB-IoT technology. Therefore the research analysis is taken from the perspective of PLN.

Research on the connectivity technology for smart metering use case has been conducted recently. Research by Wibisono [4] in PLN Bali Indonesia, discussed the feasibility of LoRa WAN as part of LPWA-based technology such as NB-IoT but in the unlicensed band category. The faster time-to-market is a factor considered by authors to choose LoRa WAN rather than other technologies like NB-IoT. This research concludes that LoRa WAN is a suitable technology to support smart metering implementation because of its characteristics, technology readiness, and its supporting ecosystem. Thus, LoRa WAN become one of the most promising technologies for PLN Bali Indonesia to implement.

In this paper, the implementation of the smart meter using NB-IoT technology will be analyzed. The analysis is using the techno-economic approach. Cost-benefit analysis (CBA) as a tool is used to determine the feasibility of this project deployment. In the end, it can be used to assist decision makers to make rational investment decision while considering the importance of technical aspects.

II. NB-IOT AND SMART METER OVERVIEW

A. Narrowband IoT (NB-IoT) Technology

Narrowband Internet of Things (NB-IoT) is a cellular connectivity technology categorized into Low Power Wide Area (LPWA) [5]. NB-IoT built from existing LTE functionalities with some simplifications and optimizations. At the physical layer, NB-IoT occupies 180 kHz of the spectrum, which constitutes a single LTE Physical Resource Block (PRB). NB-IoT can be deployed in three operation modes (1) In-Band mode : an only NB-IoT carrier utilizes the bandwidth of one existing LTE Physical Resource Block (PRB), (2) Guardband mode : NB-IoT will deploy in the unused resource block within the guardband of an LTE carrier, and (3) Standalone mode: single NB-IoT carrier is implemented using existing idle spectrum resources [6].

Fig. 1. NB-IoT Deployment Scenario [6]


44

Improving Overall Equipment Effectiveness (OEE)

through System Dynamics and the Internet of

Things (IoT)

Yunizar Zen




Depok, Indonesia

[email protected]

M. Dachyar




Depok, Indonesia

[email protected]

Farizal




Depok, Indonesia

[email protected]

Abstract—Lately, Industry 4.0 has become one of the

technological focuses that are still trying to be achieved,

especially by developing countries. Indonesia as a developing

country has five priority industry sectors that are prioritized to

develop, The Food and Beverage Industry, Chemical Industry,

Textile, and Apparel industry, The Automotive Industry, and

The Electronics Industry. One of the principles of Industry 4.0

which will be put forward in this paper is Interoperability, the

ability of computer systems or software to change and utilize

information, machines - sensors and humans can connect and

communicate with each other. This paper aims to improve the

bottom line of companies by delivering actionable and real-time

information, increasing productivity, reducing downtime, or

enhancing communication. Overall Equipment Effectiveness

(OEE) method is used for measuring manufacturing

productivity. By measuring OEE and the underlying losses, we

can get pieces of information to improve or fix the production

process and set a higher OEE target. A case study was

conducted in an automotive company which focuses on the

production of automotive components.

Keywords—Industry 4.0, Internet of Things (IoT), Overall

Equipment Effectiveness (OEE), System Dynamics

I. INTRODUCTION

According to the IoT Business Platform [1], there are 5 priority industrial sectors in Indonesia which will be prioritized to develop first, which are among others The Food and Beverage industry sector, The Chemical industry, The Textile and Apparel industry, The Automotive industry, and The Electronics industry.

The fifth contribution of this sector to the national Gross Domestic Product (GDP) is currently 17.8%. Minister of Industry Hartanto said the five sectors would be the key to driving the added value and high technology of the downstream industry to become competitive players in the new global context. The design of the "Making Indonesia 4.0" Roadmap involves stakeholders from various segments, including government, industry players, industry associations, technology companies, and research bodies and educational organizations.

The development of motorized vehicle use in Indonesia places Indonesia in the third position of motorcycle sales in the world. While the first and second positions were occupied by China and India, which reached 25 million and 12.5 million units per year. Indonesia has an average sales number of 6

million units per year. This is reinforced by data from the Central Statistics Agency which outlines the development of the number of motorized vehicles in Indonesia by type, which noted the number of motorbike vehicles in Indonesia in 2017 was 113,030,793 units, while for cars only 15,493,068 units [2].

To be able to meet market needs every year, it is needed not only the appropriate amount of production but also spare parts that are ready for use. Productivity improvement can be done by Overall Equipment Effectiveness (OEE) analysis. The results obtained from the OEE calculation will be a measure of the increase that must be made by the production and maintenance department. Based on the results of the OEE data, improvements will be made by utilizing the technology of industrial 4.0, which will trigger an increase in machine productivity to achieve production targets.


A. Industry 4.0

Industrial development from year to year has reached industrial level 4.0, making the manufacturing industry and other industries competing to migrate their systems into an Internet of Things (IoT) based system. In the last 10 years, IoT has begun to get more attention because of the growing needs of technology and IoT is considered to provide promising opportunities to make systems and applications in the industry stronger as an extension of factory automation. Design and manufacturing operations involve various types of decision making at various levels and domains. Complex systems and having a number of design and decision variables require real-time data collected directly from machines, processes, and the business environment. Enterprise systems (ES) are used to acquire data, communication, and decision-making activities [3]. Therefore, infrastructure information technology greatly influences the acquisition and retrieval of data for ES. In the automotive industry, IoT is widely used in the production line, quality monitor and control, assembly line, logistics and product (or part) tracking, and the real-time link of customer service [4].

B. Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness (OEE) is a simple metric that is able to show the status of the current manufacturing process and also the complex tools that make it possible to understand the effects of various problems in the


45

Implementation of ISO 9001 in Indonesia

Automotive Component Manufacturing Industry

Zulfadlillah



Universitas Indonesia Salemba

Rahmat Nurcahyo




Depok [email protected]

Djoko Sihono Gabriel




Depok

Abstract— In the globalization era, companies must focus

on the quality of products and services to improve their

competitive advantage. The implementation of ISO 9001

quality management system is important to survive in the

fierce competition. Indonesia as a developing country has a

fluctuating number of companies that implement ISO 9001.

The aim of this research is to examine the impact of ISO 9001

on operational performance and business performance of

automotive component manufacturing industry. The literature

study was conducted to obtain ISO 9001 principles, operational

performance criteria and business performance criteria that

will be used as variables in this research. The data was

collected using questionnaire and analysed by experts in the

manufacturing industry. The result of this research shows that

ISO 9001 can improve performance of the company.

Keywords—ISO 9001, Operational Performance, Business

Performance, Manufacturing Industry

I. INTRODUCTION

In the globalization era, companies must focus on the quality of product and service to improve their competitive advantage. Quality is one of the competitive strategy to increase the performance of companies in the global market. Therefore, it is important for companies to continuously develop the quality of the product or service produced.

ISO 9001 is an international standard of quality management system that aim to guarantee that the organization will provide product or service that meet the customer requirement [1]. ISO 9001 being one of the most widely used by company in implementing quality strategies across the world. Moreover, ISO 9001 has become a subject of focus in many developing countries and countries that are classified as emerging market ones [2]. The first of ISO standard was published in 1987 by the International Organization for Standardization based in Geneva, Switzerland. In 2015, ISO 9001 was reviewed and the latest version was introduced, namely ISO 9001:2015 which emphasize on the process approach and risk-based thinking that aim to make the process stronger.

Based on ISO survey data, in Indonesia there were 7,287 industries which have implemented ISO 9001 in 2017 [3]. The number of companies which have implemented ISO 9001 within 2011-2017 are presented in Fig 1.

The manufacturing industry is one of the priority sector that drive national economic growth. The Ministry of Industry stated that the manufacturing industry, especially the non-oil and gas processing industry, play an important role in accelerating national economic growth.

According to data from BPS (Central Bureau of Statistics) in 2012, the contribution of the manufacturing industry sector to the national economic reached 17.99%, reached 17.74% in 2013, reached 17.89% in 2014, and in 2015 the contribution of the manufacturing industry sector to the national economic reached 18.18%. According to the data of the United Nation Statistics Division, Indonesia was ranked fourth of 15 countries in the world that manufacturing industries contributed significantly to Gross Domestic Product (GDP) in 2016. Indonesia was able to contribute up to 22% after South Korea (29%), China (27%) and Germany (23%) [4].

The export value and import value of the Indonesia manufacturing industry are presented in Fig 2. In 2015, the export value of the manufacturing industry reached 108.6 billion USD and the import value of the manufacturing industry reached 109.5 billion USD. In 2016 the export value of the manufacturing industry reached 110.5 billion USD and the import value of the manufacturing industry reached 108.2 billion USD. In 2017 the export value of the manufacturing industry reached 125.1 billion USD and the import value of manufacturing industry reached 125.1 billion USD [5].

The number of large and medium manufacturing companies are presented in Fig 3. The number of large and medium manufacturing companies within period of 2010-2015 always increases. In 2011 the number of large and medium manufacturing industry companies increased by

Fig. 1. The number of companies that implement ISO 9001 in Indonesia


46

Multicriteria Decision Approach for Selection

of Fault Current Limiters TechnologyHandrea Bernando Tambunan

Transmission and Distribution Department

PLN Research Institute

Jakarta, Indonesia [email protected]

Aristo Adi Kusuma



Jakarta, Indonesia

[email protected]

Putu Agus Aditya Pramana




Nur Widi Priambodo



Jakarta, Indonesia

[email protected]

Brian Bramantyo Satriaji D A Harsono




Buyung Sofiarto Munir




Abstract—Java Bali power grid become larger because of

continous development of power plant. In the other hand,

higher power generation leads to higher short circuit current

level. One of the solutions to limit this higher fault current is by

installing of the fault current limiters (FCL). Selecting the FCL

technology is relative complex as it involves multi criteria

decision making. To solve it, this study used the analytic

network process (ANP) method to find out the priority of FCL

technology with respect to the major criteria such as

technology, engineering, economic, and impact factor. Seven

alternative of FCL technology used in this study were air core

reactors (ACR), iron core reactors (ICR), saturable core fault

current limiters (SCFCL), resistive superconducting fault

current limiters (RSFCL), inductive superconducting fault

current limiters (ISFCL), and pyrotechnic current limiters

(PCL). Each FCL technology was evaluated and ranked from

each stated criteria. FCL technology alternative for each

voltage level was presented in this paper to give better

justification for FCL technology selection.

Keywords— multi criteria decision, analytic network process,

fault current limiters, current limiting reactors, solid state,

saturable core, superconductot, pyrotechnic

I. INTRODUCTION

Indonesia economic growth poses an increasing demand for electrical power. To satisfy the rise of national electricity demand, one of government’s strategic program is providing additional 35 Gigawatt (GW) power generation. Electricity supply business plant (RUPTL) presents the plan of PLN as state-owned company in electricity sector to provide the availability of electricity from 2018 to 2027 [1]. With continuous development of power plant, the scale of Indonesia’s interconnceted power system become larger. Higher capacity of power generation will results in higher short circuit current.

Short circuit current level increase will lead to serious challenge to power grid nowadays. High fault current flowing through the electrical equipment during short circuit may melt the conductor and isolation and also damage another high voltage equipments such as circuit breaker, transmission line, busses, transformer, and etc. PLN should take effective solution to mitigate this problem. Several conventional methods to mitigate high short circuit level problem are replacement of the protecting devices, reconfiguration of the network, applying higher voltage level to the system, and also using high impedance transformers [2].

Another solution to mitigate high short circuit current problem is by using fault current limiters (FCL) equipment.

The main advantages of FCL technologies such as low impedance during normal condition and high impedance during fault [3]. FCL is not design to completely supress the short circuit current, but rather reducing the short circuit current to certain level which can be withstood by existing equipments. Some recent studies of FCL technology can be found in several work [4]–[6]. Some technologies can be used such as semiconductors, saturated core, high power superconductors, and also series reactors. These various types of FCL technology can be clasified by their principle of operation and technology.

Decision making is very important and complex process. In order to aid decision maker to make the right choice, quantitative method that are used to improve the overall quality of solution. This method widely used in the branches of science [7]–[9]. Based on study [10]–[12], decision making tool are applied to help managing board of electrical company to decide a important choice. There are not many study used decision making tool to select new technology espesially for fault current limiter technology.

The purpose of this study is to find the best FCL technology for each voltage level in PLN to mitigate high short circuit current level problem by using the multi-criteria decision making method that is analytic network process (ANP) with respect to the four major criteria such as technology, economic, and impact factor.

II. SYSTEM, SHORT CIRCUIT, AND FAULT CURRENT LIMITER

OVERVIEW

A. Java Bali Interconnected Power System

Java Bali power system is the largest power system grid in Indonesia. It was reported in [13], PLN and subsidiary companies operated about 5.389 generating units with total installed capacity approximately 39.651,79 MW which 28.725,53 MW (72,44%) was installed in Java Bali power system. Java Bali interconnected power system consist of four region namely: Banten and DKI Jakarta, West Java, Central Java and DI Yogyakarta, and East Java and Bali. The electical load in this system grows annually. The electric power line 500 kV, 275 kV, 150 kV, and 66 kV voltage levels are being used in transmission line while ≤ 20 kV are used in distribution lines. The rise of system capacity would also lead to failure in power system, especially during short circuit event.


47

Performance Measurement System Development

Using SCOR-Balanced Scorecard Integrated Model

for SME in Indonesia: A Case Study for MTO

Products in Textile Industry

1Huria Nusantara

School of Industrial and System

Engineering

Telkom University

Bandung, Indonesia [email protected]

2Ari Yanuar Ridwan


Engineering

Telkom University


3Widia Juliani


Engineering

Telkom University


Abstract— The growth of SMEs in Indonesia is increasing year

by year. This indicates the business competition is increasingly

fierce and SME are required to be able to survive in a

turbulent business environment. Besides the limited resources

and many issues faced by SME, through evaluation of supply

chain performance, SME can make a better business profit

model. The measurable KPI (Key Performance Indicator) or

performance metrics can evaluate and assess the overall

performance of the company with the aim of being more

competitive, agile, reliable and able to increase the profit

margin [3] in every period that has been decided. Therefore,

this study presents the application of integration of the two

strategic models, Supply Chain Operation Reference and

Balanced Scorecard. The SCOR model is a model reference

providing a measures which can capture the performance of

many activities of the various entity in supply chain in at the

operational level of the company while the Balance Scorecard

in its application is at the strategic level of the corporate

corporation, the two models provide results in the form of

performance metrics that are used as a measure to evaluate

and assess the performance of the company.

Keywords— Performance Measurement, SMEs, SCOR-BSC

Integrated Model, Performance Metrics

I. INTRODUCTION

The number of businessmen in Indonesia is always

increasing every year, noted by the Ministry of Cooperatives

and SMEs of the Republic of Indonesia that growth of

SMEs in Indonesia in the amount of 13.98% or as many as

7,716,680 SME units in Indonesia year 2012-2017. In this a

turbulent business environment [1], SMEs are also

challenged to be able to survive in global competitive

market with all the limitations they have. The limitations

and issues had to be faced that generally occur in SMEs

include ad hoc forecast, lack of strategic approach in

procurement, more internal focus and lack of supply chain

knowledge, lack of standardization, and higher inventory

due to frequent change in demand [5]. The SMEs,

especially in sector textile industry, needs the right supply

chain strategy to create a product and provide fast

information, considering many complicated channels

involved from upstream to downstream and delivering

product to end customers [15]. Under these condition, it is

being necessary of modelling supply chain to obtain a better

efficiency and allow the company to evolve with the market

and sociotechnical environment [4].

Evaluating supply chain performance is critical to make

better business profit model [5]. The measurable KPI (Key

Performance Indicator) or performance metrics can evaluate

and assess the overall performance of company activities

with the aim of being more competitive, agile, reliability

and able to increase the profit margin [3] in every year. In

this research, the author proposed a model approach by

integrating the SCOR model (Supply Chain Operation

Reference) from APICS version 12.0 with the BSC

(Balanced Scorecard), within the scope of the discussion is

production process. SCOR is a reference model developed

to describe business activities linkages of all elements in

demand satisfaction beginning with the initial signal

demand (the order or forecast) until the ending signal of

demand has been satisfied. The model contains of six

primary management processes of Plan, Source, Make,

Deliver, Return, and Enable [2]. The SCOR model also

presents KPIs requires to assess Supply Chain performance

that helps linkage between business objectives (strategic and

tactical) and supply chain operation [5]. While the use of the

BSC (Balanced Scorecard) is to develop a strategic map of

the overall activities of the company so that it is aligned

with the company's goals [12]. The overall activities which

are related to the different classes of business performance

financial and non-financial, internal and external. Thus, the

use of integrated SCOR and BSC is to ensure the greater

effectiveness performance measurement for SME [5].

II. METHODOLOGY

A. Data Collection

In this step of research, the required data were collected. Interview and discussion were used to gain the data. The respondents are the head of the company and head of the production. The data required are:

• Actual business process of production;

• Stakeholder/ Role Player of the company;

• Supply chain objective;

• Company’s objective; and

• And the factor required in the weighting process.

All the data above are used as an input, where the input will

be processed based the systematics have been studied.


48

Six Sigma for Evaluating Electronic Signature in

eProcurement System: A Case Study

Eko Cahyo Nugroho Computer Science Department,

BINUS Graduate Program - Master of

Computer Science

Bina Nusantara University

Jakarta, Indonesia 11480

[email protected]

Herry Saputra Computer Science Department,


Computer Science



[email protected]

Davy Yeria Gunarso Computer Science Department,


Computer Science



[email protected]

Antoni Wibowo

Computer Science Department,


Computer Science


Jakarta, Indonesia 11480 [email protected]

Ditdit Nugeraha Utama

Computer Science Department,


Computer Science


Jakarta, Indonesia 11480 [email protected]

Abstract—Six-sigma is an approach to appraise a

company’s prospect in generating a number of piece with homogenized processes without any production defects or zero faults. It is operated not only for declining defect numbers, but also for reducing the company’s imperfection itself. It means, company is able to decrease the cost (in term of money or time), also to increase the efficiency by evaluating current process and planning to improve company management process. In this study, six-sigma benefited to determine the success level of implementation of e-signature application in e-procurement system based on user experience rate. Conclusively, the implementation of “change management” has enhanced the business process in detail, while it was still in the equivalent sigma level 2.

Keywords—six sigma, DMAIC, change management,

electronic signature, electronic procurement, user experience

I. INTRODUCTION

In a business process, for every company, time is practically counted as a cost which is consumed. Thus, in using internet and computer technology, time consumption is one of objectives to be reduced. Through a faster business process, companies are able to enlarge their productivity level consequently increasing their revenue [1][2][3]. Moreover, in the public service companies or government, faster business process can also increase the public or customer satisfaction [4]; where company’s internal business process measurement should be essentially performed before conducting a measurement relating end user or customer.

In this paper, a case study of the electronic signature (eSig) implementation in one of Government University in Indonesia was taken, which is SBM ITB Jakarta Campus. This eSig process is employed in eProcurement system used to propose the purchase order from department unit to procurement unit via internet and web-based application. Nevertheless, mostly hard signature is also considered as the legal document even the approval process has been done directly thru the system. Therefore, before the eSig applied, every department members should print an approved purchase order to be signed by authorized person. The document is necessitated by finance unit as a required

document. Hence, via eSig implementation, the signature process is done directly via system without printing any documents. The target is to decrease a time of process which is described by the user experience.

Thru measuring user experiences which are involved in the procurement approval process, an achievement of eSig implementation can be academically treasured; it represents a success or failure of company’s business change management. The user experience measurement was methodologically calculated using six-sigma framework; and to collect empirical data, questionnaire spreading which was sent in two periods (before and after implementation) was performed. The result delivered sigma level between before and after implementation.

II. THEORETICAL SIDE OF SIX SIGMA

Six-sigma is a fact capacity as a measurement result of the company’s performance in their products or services. Sigma denotes a statistic standard deviation and reflects a deviation degree. In the process of production or service, six-sigma is operated to depict an excellence variability and to indicate a quantity of data in the conservatory of excellence requirements and customer necessities. Six-sigma signifies six-time standard deviation between average and lower or upper limit; temporarily, the instability is decreased and only 3.4 defective parts per million opportunities (namely 3.4 ppm) is obtained [2][5].

Six-sigma approach was established by Motorola in 1987, and then it was broadly embraced by several big corporations (e.g. GE, Kodak, and Allied-Signal Inc.). Owing to the extraordinary advantages it brought, this approach has portrayed much helpfulness for corporations across the world.

The heart of six-sigma is DMAIC, which respectively signifies definition, measurement, analysis, improvement, and control. DMAIC is the elementary rational construction for realizing the six-sigma approach, and its metric system is the most distinctive fragment of this approach [5]. The major benefit of the six-sigma metric is regarding its flexibility in performance assessment. It means, it is able to


49

Protection System Failure on 150kV Transmission

Line in Java-Bali Grid due to Fault Current Residual

Aristo Adi Kusuma

Transmision and Distribution

Department




Transmission and Distribution

Departement





Departement



Abstract—Reliability of measurement apparatus is an

important thing in order to secure the continuous supply of

power in the system. Measurement error potentially affects the

protection system performance. In the study, an example of

protection system failure due to measurement error on current

transformer (CT) on one of the 150kV transmission line in

Java-Bali grid will be discussed. The failure occurs due to

residual fault current that remain flow at the faulted phase

even though fault clearing has been done by the primary

protection of the transmission line. Therefore, the study will

determine the cause of fault residual current measured by CT

after fault clearing by performing simulation in application of

transient analysis. In this study, the data during single phase to

ground fault on the transmission line are presented. Along with

the explanation of literature study related to measurement

error on CT. After that, simulations using variation of CT

measurement condition are performed.

Keywords—measurement error, CT, residual fault current

I. INTRODUCTION

The most common voltage level on transmission lines in Java-Bali grid is 150kV. During the operation, short circuit fault on transmission line due to lightning surge or switching surge or foreign object contact is often found [1][2]. Therefore, coordination of protection system is needed to improve the reliability of the 150kV transmission lines. An important factor in coordination of protection system is utilization of current transformer (CT) and voltage transformer (VT) with appropriate rating. In addition, the reliability of those measurement apparatus also needs to be considered, especially related to measurement error. Measurement error on CT could be the cause of protection system failure as stated in [3]-[6], while the effect of measurement error on VT to distance relay is discussed in [7][8]. Measurement error due to saturation of CT would cause delay time operation for over current relay and distance relay, because CT had to wait the DC offset to die out first [5,6]. Saturation of CT also reduce the sensitivity of line current differential [6], and also cause distortion of current waveform [9]. However, there was no study that focused to discuss about the effect of residual current of CT to protection system and the causes of residual current presence on CT.

The study will discuss about protection system failure due to measurement error on CT, where it is occurred on one of the 150kV transmission line in Java-Bali grid. Protection system failure occurred when there was single phase to ground fault on the transmission line. Three types of protection relay have been used in the transmission line. Line current differential as main protection, distance relay as remote backup protection and over current relay as local backup protection. In addition, single pole auto reclose has

also been applied on the transmission line. During the occurrence of single phase to ground fault, the main protection had worked properly on both sides of the transmission line. However, the CT was still measuring the presence of fault residual current with high DC offset on the faulted phase after fault clearing. This phenomenon led to healthy phases trip due to distance relay zone 2 trip on one side of the transmission line and pole discrepancy operation on the other side. Therefore, this study will determine the cause of fault residual current measured by CT after fault clearing by main protection.

II. METHODOLOGY

In this study, the data during the occurrence of single

phase to ground fault on the 150kV transmission line are

presented first. The data include the single line diagram of

the system, the tower geometry of 150kV transmission line,

lightning detection system data and fault recording data of

protection relay on both sides of substation. The explanation

of literature study related to measurement error on CT is

also presented. It is assumed that CT measurement error is

the initial indication that is considered as the cause of fault

residual current presence. Based on data and literature study,

then simulations using application of transient analysis are

performed. Variation of CT measurement condition, ideal or

non-ideal, were carried out during simulations.

III. FIELD AND FAULT DATA

Single line diagram of the 150kV transmission line is

given in Fig. 1. Based on Fig. 1, it is known that the busbar

configurations of substation connected by the transmission

line are double busbar single breaker (substation A) and one

half breaker (substation B). Substation B is power plant

substation, where the utilization of one half breaker will

result to isolation time addition due to the tripping time

delay between diameter breakers. Whereas substation A is

load substation, where there are busbars with two different

short circuit levels and both of them are connected with air

core type reactors. The 150kV transmission line from

substation B to substation A predominantly uses tower

geometry with four circuits and two ground wires. The

studied circuits are located at the bottom of the tower. The

tower geometry is then transformed into tower with only

two circuits and two ground wires before heading to

substation A. The type of conductor used in the 150kV

transmission line is thermal-resistant aluminum-alloy

conductor aluminum-clad steel reinforced (TACSR) 2x410

mm2, with a total length of 5.89 km/circuit. The magnitude

of short circuit impedance that represents the strength of

substation A and B is given in Table I.


50

Data Warehouse Development for Credit System

Tiffany Tantri Computer Science Department, BINUS Graduate Program-

Master of Computer Science, Bina Nusantara University,

Jakarta, Indonesia 11480,

Email : [email protected]

Abba Suganda Girsang

Computer Science Department, BINUS Graduate Program-




Herman Gunawan Computer Science Department, BINUS Graduate Program-




Lie Maximiliamus Maria Kolbe Computer Science Department, BINUS Graduate Program-




Narada Thiracitta Computer Science Department, BINUS Graduate Program-




Sani Muhamad Isa

Computer Science Department, BINUS Graduate Program-



Email : sani.m.isa @binus.ac.id

Abstract—A company credit that provides for loan and

payment credit is expected to have the business intelligence to to

take decision. It needs the statistical data to take the action. This

paper aims to build data warehouse technology which can provide

customers behavior in statistical data by using Kimball method.

The result of the data warehouse will be report or dashboard. It

can represent the useful information for company. The data

warehouse system that is created is be able to provide the company

the accurate data that will be used to choose the best decision based

on customer behavior. Some information can be generated from

this data warehouse which is represented in fact table and its

dimensions.

Keywords data warehouse; star schema; credit company;

business intelligence;

I. INTRODUCTION

Currently, credit company which focuses for loan and

payment credit grows very fast and need to take the best

decision in various data. Therefore, the company cannot only

use the transactional data to get the accurate information. The

transactional grows very fast and needs the significant time to

process it. Data warehouse is a technic to make data that contain

history could be used to make an analysis to support a decision-

making system. The data itself could be taken from different

sources. Normally, data warehouse contains 3 parts, which are

OLAP database system, in-depth data analysis and data

visualization [1]. Kimball approach has been used in multiple

area to provide solutions related to decision-making system [2].

The reason is because of the efficiency of the data structures,

the area of development of the data structures, and the method

in its design [3].

Data warehouse has an architecture itself as the various

company and data which will be analysed. There are many

researches in data warehouse problem. For example, Ishita Das

used data warehouse to improve the decision-making system in

bank for loan disbursement sector [4] , Sue L. Visscher used

data warehouse to create a service-level, standardized

healthcare cost data [5], Hanamant B. Sale used data warehouse

to prevent crime [6], Olugbenga Adejo used data warehouse to

predict the student performance in higher educational

institution [7] , Stone M. David used business intelligence with

customer insight to support interactive marketing [8], Desheng

Dash Wu used business intelligence in risk management [9],

Saeed Rouhani that researched about the connection of business

intelligence and enterprise system [10], and Hussain Al-Aqrabi

that researched about the connection of business intelligence

and could system [11].

The purpose of this research is to provide credit company

statistical data that could help in decision-making system. The

data will be based on customer behavior. In the future, by using

the result of this research, a credit company will be able to make

decision that will increase its reputation.

II. TEORITICAL BACKGORUND

A. Business Intelligence

Business intelligence system is a process to help decision

support system based on available data [12]. Therefore,

managing data becomes the most important thing in business

intelligence. Business intelligence works by using raw data and

transforms it into new information or new knowledge to be used

by decision maker [13]. For business intelligence to work, there

are three areas to be specialized, Analytical Skills, Information

Technology (IT) Knowledge and Skills, Business Knowledge

and Communication Skills [14].


51

Improvement Priority Analysis of Indonesian

Tourism Special Economic Zone

Eki Ludfiyanti




Depok

[email protected]

M. Dachyar*



Universitas Indonesia Depok

[email protected]

*corresponding author

Rahmat Nurcahyo




Depok

[email protected]

Abstract—In recent years, the tourism industry continues

to grow and has become one of the largest sectors of the world

economy. This also happened in Indonesia, domestic and

international tourist increasing significantly from year to year.

In 2017, tourism is the second largest contributor of foreign

exchange in Indonesia. Moreover, in 2019 it will be planned as

the main industry of Indonesia. In line with the intention to

build and develop the tourism industry, the government

already made “10 Priority Tourism Destination Strategy”, 4

out of 10 destinations has become Tourism Special Economic

Zone (SEZ). Tourism SEZ aims to accelerate regional and

national economic development through that regional tourism

potential. It is hoped that through those strategies it can

accelerate the development of tourist destinations in Indonesia,

then make Indonesia as a country with a highly competitive

tourism industry among other countries. This study aims to

analyze improvement priority by evaluating Tourism

Destination Competitiveness (TDC) dimensions and criteria in

2 destinations: Tanjung Lesung and Mandalika, using Delphi,

Importance-Performance Analysis (IPA), and DEMATEL

within 2 perspectives (experts and tourists). The results of this

study are expected to provide an analysis for improvement

priority of the tourism industry and to provide new framework

for improvement and development not only for those two

destinations but also for other tourism destinations in

Indonesia.

Keywords— Tourism Special Economic Zone (TSEZ),

Tourism Destination Competitiveness (TDC), improvement,

Delphi, IPA, DEMATEL.

I. INTRODUCTION

In Indonesia, tourism has now developed very rapidly.

Tourists both domestic and international continue to

increase every year [1] and in 2017 tourism was the second

largest contributor of foreign exchange to Indonesia [2]. The

receipt of foreign exchange from tourism is also expected to

continue to increase and be the highest compared to other

key sectors such as oil & gas, CPO, coal, and rubber. It is

planned that in 2019 tourism will be made as Indonesia's

main business [3].

In line with the government's intention to develop

national tourism, in 2017 Government Work Plan, Tourism

is the fourth order of priority development sectors after food,

energy, and marine. In 2016 the government made a strategy

of 10 priority tourism locations in Indonesia for the next 5

years: Lake Toba (North Sumatra), Tanjung Kelayang

(Bangka Belitung), Thousand Islands (DKI Jakarta),

Mandalika (West Nusa Tenggara), Tanjung Lesung (Banten),

Borobudur (Central Java), Bromo Tengger Semeru (East

Java), Wakatobi (Southeast Sulawesi), Morotai Island (North

Maluku), and Labuan Bajo (East Nusa Tenggara).

There are 4 destinations declared as Tourism SEZ, i.e

Tanjung Lesung, Mandalika, Morotai, and Tanjung

Kelayang. The purpose of the Tourism SEZ is to accelerate

the achievement of regional and national economic

development through the tourism potential of the region. Of

the four Tourism SEZ, it is known that Tanjung Lesung is

the first destination that get status as a Tourism SEZ (2012),

while Mandalika and Morotai in 2014, and the last is

Tanjung Kelayang in 2016. And of the four locations, there

are two locations already operating: Tanjung Lesung and

Mandalika, while Tanjung Kelayang and Morotai are still in

the development stage [4].

Ministry of Tourism Performance Report in 2017 showed

that Tanjung Lesung experienced a decline in foreign tourists

by 19% and Mandalika only increased by 2% compared to

2016 [5]. In 2017 and 2018, Minister of Tourism of

Indonesia admitted that the target number of foreign tourists

had not been reached. Seeing the results of low achievement

whereas both destinations are already in operating stage, it

can be concluded that tourism development in that

destinations is still not optimal so that needs to be evaluated

and improved in order to increase the number of tourists.

The ability to increase the number of tourists by

providing a satisfying experience is called Tourism

Destination Competitiveness (TDC) [6]. And it is crucial for

a destination or tourist area to evaluate their competitiveness

attributes as a potential factor that will influence tourists in

choosing tourist destinations [6]. In addition, it is proven that

TDC can affect the level of tourist loyalty towards a

destination [7] and the success of a tourist destination [6].

Based on those explanations, one effective way to

increase the number of tourists is by evaluating the value and

criteria analysis of TDC. Therefore, this study carried out an

improvement priority analysis by evaluating Tourism

Destination Competitiveness (TDC) dimensions and criteria

in 2 destinations: Tanjung Lesung and Mandalika, using

Delphi, Importance-Performance Analysis (IPA), and

DEMATEL within 2 perspectives (experts and tourists). The

final results of this study is providing an analysis for

improvement priority design of the tourism destinations and

also providing a new framework for improvement and

development not only for those two destinations but also for

other tourism destinations in Indonesia in the future.


52

Power System Inertia Estimation Based on

Frequency Measurement

Joko Hartono


Department





Department





Department


Jakarta, Indonesia

[email protected]

Aristo Adi Kusuma


Department



Abstract— The magnitude of system inertia determines the

rate of frequency change if there is any power deviation in the

system. Thus, estimation of inertia magnitude is essential to be

performed in order to obtain accurate defense scheme when

the system is interrupted. In addition, the inertia magnitude

can also be used to determine the magnitude of power

deviation, so that defense scheme can perform load shedding

more accurately. Therefore, this study will discuss about the

method for estimating system inertia using Artificial Neural

Network (ANN) only with frequency measurement data. The

training and validation data for ANN are obtained from the

simulation results of power swing equation. After that, the

ANN network is used to estimate the inertia magnitude of

isolated system from the real measurement data. The results

show that the estimation of system inertia and power deviation

only with frequency measurement data will have magnitude

that close to the real measurement results. Furthermore, this

method for knowing the system inertia will be used in the

company to determine the proper defense scheme for different

system inertia.

Keywords— system inertia, estimation, power deviation,

ANN

I. INTRODUCTION

Maintaining the system frequency in the nominal range is essential in the power system. National standard requires that the permissible range of frequency change under normal condition is 49.5 Hz-50.5 Hz [1]. The dynamics of frequency change are strongly influenced by the system configuration that is represented by the inertia magnitude.

When generator with a mass is rotating, then the rotational energy will be stored into kinetic energy and its energy is influenced by the generator inertia [2]. Generator with higher inertia will be more difficult to experience rotational speed deviation during any power reduction or power increment in the system.

In the power system, inertia magnitude is very influential to the rate of frequency change if there is any deviation of mechanical power of generator or electrical power of load, which follow the power swing equation [3]. If the mechanical power of generator is higher than the electrical power of load, then the system frequency will increase. Otherwise, if the electrical power of load is higher than the mechanical power of generator, then the system frequency will decrease. In the case of power deviation with same

magnitude, system with higher inertia tend to have slower frequency change compared to system with lower inertia.

If power system is connected with many rotating machines such as coal thermal power plant, then the system inertia will be higher. Thus, the frequency change will be slower if there is any power deviation in the system. Therefore, the control system of power plant will have more time to respond the frequency change that occurred in the system. Otherwise, if the power system has low inertia (due to penetration of many renewable energy in the system [2][4]), then the frequency change might be faster than the response of frequency control system. As a result, it will potentially cause system collapse [5][6].

There are several researchers who have performed study related to inertia estimation in the generation system. Determination of system inertia using phasor measurement unit (PMU) is given in [7][8]. The results of study show that PMU can determine the magnitude of inertia that is strongly influenced by the characteristics of generator and load. Determination of system inertia using relation between power deviation and frequency change before and during any disturbance is given in [9] and [10]. The results of the study show that estimation of system inertia will be more accurate if it is performed on the instantaneous data after the occurrence of disturbance. Determination of system inertia using closed-loop identification method is given in [11]. The results of the study indicate that this method can be performed online and it has low risk on system security. Determination of system inertia using power demand estimation method is given in [12]. The results of the study show that the system inertia can be identified through the data of load deviation and frequency change.

This study will discuss about the method for determining the system inertia only by using frequency measurement data, which is then processed with Artificial Neural Network (ANN).

II. INERTIA OF POWER SYSTEM

System stability is the ability of system to remain stable if

there is any small or large disturbance in the system.

Stability studies which evaluate the impact of disturbances

on the electromechanical dynamic behavior of the power

system are of two types, transient and steady state [13].

Steady state stability is the ability of system to accept small


53

Pattern Recognition using Machine Learning for

Cancer Classification

Anisah Andini


Institut Teknologi Bandung Bandung, Indonesia

Septasia Dwi Angfika



Bandung, Indonesia

Isa Anshori




Betty E.Manurung






Bandung, Indonesia

Marvel Sugi






Bandung, Indonesia

Abstract—This paper presents the application of machine

learning on gene expression datasets in order to classify cancer

cells. Several analytical methods, including Principal

Component Analysis (PCA), Support Vector Machine (SVM),

Gradient Boosting, and XGBoost are performed to find the

best model for processing the datasets. Additionally,

classification with hyperparameter tuning using GridSearch

and RandomSearch are also performed. The dataset is

obtained from the study published by Golub et al [1]. They

reported how new cases of cancer could be classified by gene

expression monitoring via DNA microarray and thereby

provided a general approach in identifying new classes of

cancer and assigning tumors to the existing and known classes.

The datasets were used to classify patients diagnosed with

acute myeloid leukemia (AML) and acute lymphoblastic

leukemia (ALL). These datasets contain measurements in

correspond to ALL and AML data samples from Bone Marrow

and Peripheral Blood. Based on the simulation results, PCA

with K-Nearest Neighbor shows the best result by providing

82% of classification accuracy.

Keywords—machine learning, leukemia, gene expression

I. INTRODUCTION

Cancer is generally characterized by abnormal growth of cells beyond their usual/normal boundaries. Cancer can affect almost any body parts particularly or simultaneously and has many types and variance that each requires specific treatments. Globally, cancer ranks the second in causing death [2]. In terms of number, it accounts for about 9.6 million of death in 2018 [2]. However, about 30-50% of cancer deaths can be prevented by well-managing key risk factors, including avoiding tobacco products, not drinking alcohol, not overweight, exercising regularly [3].

In 2014, there are about 195,300 deaths caused by cancer in Indonesia [4]. The most common cancer type are trachea, bronchus, and lung cancer. These types of cancers caused 21.8% deaths among male [4]. There are some factors that caused cancer for male, such as tobacco smoking, alcohol consumption, and physical inactivity. While breast cancer is the most common cancer among women and accounted for about 21.4% of death [5].

To significantly suppress death by cancer, early diagnosis, accurate screening, and proper treatment become

very crucial. The cancer treatment options may include surgery, taking medicines, and radiotherapy. To properly treat the cancer, the treatment has to target specific types of the tumor, so that efficacy can be maximized while minimize toxicity. Before targeting a specific tumor types, cancer classification is needed. However, current cancer classification has serious limitations. Moreover, cancer classification has been always difficult because it relies on historically specific biological insights and interpretation, rather than systematic and unbiased approaches [1].

Cancer based on gene expression has been one of intensive and trending research topic in cancer classification. Numerous works have successfully provided valuable information for discrimination between normal and cancer cases. Nonetheless, the classification task is usually not easy because there are typically thousands of expressions with few dozens of cases [6]. In this paper, several classifiers based on machine learning are used to perform cancer classification. The simulation results will provide the most suitable and accurate method in classifying cancer.

This paper has the following structure. General background is described in section 1. In Section 2, the methods for each classifier is described. Section 3 shows the results of each methods along with the discussion. And finally, the conclusion of the paper is provided in Section 6.

II. METHODS

The dataset for this paper comes from a study published

in 1999 by Golub et al [1]. It showed how cancer could be

classified by gene expression (via DNA microarray) and

provided a general approach for identifying new cancer

classes and assigning tumors to known classes. These data

were used to classify patients with acute myeloid leukemia

(AML) and acute lymphoblastic leukemia (ALL).

There are two datasets, i.e. initial (training, 38 samples) and independent (test, 34 samples) dataset. These datasets contain measurements corresponding to AML and ALL samples from Bone Marrow and Peripheral Blood. The data is used to classify the type of cancer in each patient by their gene expression. Following are the methods used in implementing pattern recognition for dataset gene expression in cancer treatment:


54

Designing Conceptual Model of An Inbound

Logistics Consolidation with Multi-Vendors Single-

Buyer in The International Supply Chain Network

Zulnio Tarakanantyo Yudha Perwira Department of Industrial Engineering Faculty of Engineering, Universitas Indonesia,

Salemba, Jakarta Pusat, Indonesia [email protected]

Abstract— Transportation is a very important aspect of

logistics, which accounts for 60% of logistics costs, while logistics

costs account for around 21% of all costs in manufacturing

companies. This is a reason for transportation to be the focus of

attention in many discussions about logistics. One of the

innovations in transportation is the shipping consolidation

strategy, which combines shipping multiple orders in one

shipment using the same vehicle to the destination. In the

previous study, the focus on shipping consolidation decisions

was more on the downstream side of the supply chain with the

scheme of relations 1:1 or 1:n, while the upstream side of the

supply chain, such as inbound logistics with scheme n:1 (multi

vendors-single buyer) is still limited. In the point of view from

the supply chain scope, international supply chains have a

higher complexity in each supply chain function than domestic

supply chains. In international supply chain with sea

transportation modes, there are high uncertainties on vessel

sailing with long lead time. Vessel speed, delay shipment, port

strike, waiting time at the port, custom clearance holds and

exams become complexities that need to be considered in sea

transportation planning. Conceptual model is designed in this

research for inbound logistic consolidation considering multi

vendors-single buyer scheme and the uncertainty of sea

transportation to capture the complexity of sea transportation

in real life. Keywords—shipment consolidation, logistic inbound, multi

vendor-single buyer, international supply chain

I. INTRODUCTION

Transportation is a very important aspect in logistics. [2]

mentions that transportation accounted for 1/3 to 2/3 of the cost of logistics. According to [3], the transportation costs take a big portion as much as 62% of logistics costs and inventory costs represent 32%. Meanwhile, according to [4], logistics plays about 21% of the total costs in manufacturing companies. This is enough reason why transportation becomes focus of attention in many discussions about logistics. One form of innovation in the field of transportation logistics is a shipment consolidation strategy, which combine the delivery of multiple orders in a single shipment using the same vehicle to some specific purpose [6]. The strategy aims to improve the efficiency of the use of vehicles in long-distance transport fleet that implement Full truckload (FTL) service. The rental fee per FTL vehicle usage is charged on the full capacity usage of the vehicle. Thus, users must send their products in the total capacity of the truck so that it can benefit from economies of scale.

In the last few years, shipment consolidation has been the subject of research that attracts much attention. The

Tueku Yuri Zagloel* Department of Industrial

Engineering Faculty of Engineering, Universitas

Indonesia, Depok, Jawa Barat, Indonesia

*Corresponding author [email protected]

basic idea behind the shipment consolidation is that a small portion of the transportation costs arising independently of the quantity or the number of products included in the order, and transportation costs can be reduced by sending multiple orders in a single shipment. [4] consider the case where the buyer ordered several products from a single supplier and consolidate several types of products within a product group. Then, this product group is booked together that lead to lower order cost and transportation cost than if each type of product ordered individually. Shipment consolidation has been studied in a variety of different scenarios, such as the case of retailers who order a lot of products in one supplier [4],[9] and the case of suppliers or logistic service providers who deliver products to several retailers [10],[11],[12].

Looking at the previous study, the focus of consolidation strategy is more in the downstream side of the supply chain, and more rarely pay attention to the upstream side in the supply chain, such as inbound logistics. [14] identified that only 7 of the 155 papers that consider more than one supplier, where several papers focus on the relationship of 1:1 or 1:n. Problems faced by the company would have been different if comparing consolidated outbound logistics from one factory to some customers or retailers with inbound logistics consolidation of several suppliers to the factory.

[4] have studied the delivery of inbound logistics consolidation of several suppliers to one buyer factory with milk run. In the case of milk run, a truck picked raw material from several suppliers, and then transmit it to the buyer. Consolidated deliveries occur at a different supplier locations where trucks transport goods, The advantage of this network is a possibility to maximize the capacity of the truck up to a very high percentage with a relatively low cost. [15] studied the joint replenishment problem (JRP) that consider the delivery constraints, budget, and transport capacity. Mode of transport used in the model is a multi-truck with fixed transportation costs. [16] examined the collaborative replenishment in the presence of intermediaries (CRI), which models the joint replenishment of some products by some buyers with mediation of intermediaries. [17] models the logistics inbound improvement using cross-docking terminal. Incoming shipment by truck supplier unloaded, sorted and loaded onto outbound trucks waiting on the dock, which then forwards delivery to their respective locations in the distribution system. [18] examined the shipment consolidation by using the regional distribution center (RDC) at perishable product. [19] examined the shipment consolidation by applying a common replenishment epochs (CRE). In this model, manufacturing distributing products to some retailers


55

A Classification of Research on New Product

Development in Small Medium Enterprises

Muhammad Iqbal



Depok, Indonesia

Industrial Engineering Study Program

Universitas Telkom

[email protected]

Amalia Suzianti



Depok, Indonesia

[email protected]

Abstract— New Product Development (NPD) is pivotal for

company’s business. Recent analysis on NPD research shows

that national-specific scope of NPD studies is important. In the

context of national economy, Small and Medium Enterprises

(SMEs) has important role, and thus this article tries to

elaborate and classify the researches of NPD in SMEs, as an

effort to gain an insight of this topic. The findings from the

study is that dominant research areas on SME’s NPD are

classified into three major areas: success-failure factors,

followed by NPD strategy, and staged process. More

emphasis should be put on topics about radical products,

ideation and creativity, and the new product development

speed.

Keywords—new product development, staged process, small

and medium enterprise, NPD success, NPD failure

I. INTRODUCTION

New Product Development (NPD) is important for an

organization because the sustainability and growth of a

company depends on the development of their products [1].

A successful new product development will contribute

positively to a firm’s gains [2]. Despite the acceptable

knowledge that NPD is essential for today’s business, the

idea of how to execute a successful NPD is still an

important and challenging issue. It is because of the risk of

NPD failure dan the increasing NPD practices that the study

of NPD is necessary [3].

Prior literature studies on NPD researches show several

findings, such as the importance of context of cross-

functional team in development phases, the strategic

alignment, and managerial conformity [4]. Later study finds

that cross-functional communication and the ability to

respond the competitive challenge have increasing effect

size on NPD success, while at the same time also revealed

the importance of study in the specific national scope [3]. In

the context of national-specific point of view, it is agreed

that Small-Medium Enterprises have significant role for

economic development of a country, since they are the

majority of enterprises in developed country [5] as well as

developing country [6]. Based on the importance of the

NPD and SMEs, it seems necessary to address on this topic

specifically.

There are numerous studies related with the product

development and innovation in SME. Kaminski, de Oliveira,

and Lopes [7] have excellent review on prior studies, as

presented in Table I.

TABLE I. PRIOR STUDIES OF PRODUCT DEVELOPMENT AND

INNOVATION IN SME [7]

Author Topic Region/

Country

Corso, et al. [8] Latest approaches in managing knowledge and choosing IT

standard for product development

Italy

Bommer and

Jalajas [9] SME's innovation sources

North

America

Keskin [10]

"Interrelationships among a firm’s

market orientation, learning

orientation and innovativeness"

Turkey

Salovau, et al.[11] The importance of strategy-

oriented aspects for innovation Greece

March-Chorda` et

al. [12] NPD's success factors Spain

Liefner et al. [13] Collaboration in the process of Innovation

China

McAdam and

McConvery [14] Barriers in innovation

Northern

Island

Bagchi-Sen [15]

Innovation and competitive

advantage

Canada

Kaufmann and

Tödtling [16] External relationships Austria

This show that SME’s NPD is an important topic, but there

is not yet a literature study that focus on this area. This

article tries to fill in the gap, with the goal to provide

insight of recent studies on SME’s NPD.

II. METHODOLOGY

The literature reviewed are research papers with the topic

of NPD in SME. The review is conducted following the

steps that use by Costa and Godinho Filho [17] that later on

adapt by Salim, Rahman, and Wahab [18] as follows:

• Step 1: Searching the articles related with the scope

• Step 2: Decide the classification and the structural

coding

• Step 3: Group the articles based on Step 2

• Step 4: Explain the results

• Step 5: Examine and synthesize the results

• Step 6: Identify the opportunity for next study

A. Searching relevant articles

The literature review is started with the searching of related articles. The article sample is collected from Scopus database. Scopus database is used because of its broad data coverage [19]. The initial search is performed using keywords “small” “medium” “enterprises” and exact words of “product development” for all articles published on international journals from 2009 to 2018. The initial search


56

An Environmental Ergonomics Review of Small

Medium Enterprises Workplace Condition in

Indonesia

Dene Herwanto



Depok, Indonesia [email protected]

Amalia Suzianti



Depok, Indonesia [email protected]

Abstract— Small and medium enterprises (SMEs) have a

large contribution to Indonesia's economic growth, however

the productivity of Indonesian SMEs is still relatively low. One

of the causes of the low productivity of SMEs in Indonesia is

the ergonomics analysis has not yet been applied, especially

environmental ergonomics. The owner or company

management often does not pay attention to the conditions of

the work environment. In addition, there is still very little

research in this field. This study is intended to provide an

overview of the working conditions of SMEs in Indonesia over

the past decade. For this purpose, it was conducted a search of

research articles related to environmental ergonomics in

Indonesian SMEs which published from 2008 to 2018. The

search results show that the working environment conditions

in SMEs, such as temperature, lighting, noise, relative

humidity, air quality, air velocity, and vibration, are almost all

outside the specified threshold value. Various efforts need to be

made to improve the working conditions of SMEs in Indonesia

and encourage SME management to pay more attention to the

environmental ergonomics aspects so that the conditions in the

workspace of SMEs become healthier, more comfortable and

more productive.

Keywords— environmental ergonomics; productivity; small

and medium enterprises

I. INTRODUCTION

Various studies show that small and medium enterprises (SMEs) have a very important role in supporting economic growth in a country, especially in times of economic crisis [1]. Based on data from the Statistics Indonesia, the number of SMEs in Indonesia in 2017 has reached at least 99%. The contribution of SMEs to Indonesia's GDP reached 60.34% with employment of 97.22% in 2017 [2].

Even though it contributes greatly to economic growth, the productivity of Indonesian SMEs is still relatively low [3, 4]. One of the causes of the low productivity is the lack of attention to ergonomic aspects [3].

One of the characteristics of SMEs in Indonesia is that they have not implemented total ergonomics [5, 6]. The lack of implementation of ergonomics has resulted in poor working conditions in SMEs, even though the ILO (International Labor Organization) has made various efforts to improve working conditions through the PIAC program [7].

The owner or company management often does not pay attention to the conditions of the work environment [8]. In line with that, the results of the study of [3] states that from

all aspects of the study in the field of ergonomics, the field of environmental ergonomics has received little attention in Indonesia. This is indicated by the small amount of research conducted in the field of environmental ergonomics [9], especially in SMEs, whereas the work in SMEs is mostly done in hot and humid places [3]. Poor working environment conditions can harm workers, reduce performance and productivity, allow the increase of defective products, increase work safety risks, and ultimately reduce customer satisfaction [10].

This study is intended to provide an overview of the working environment conditions in Indonesian SMEs based on environmental ergonomics aspects over the past decade. Through this study, it is expected to find out how much attention of company management to environmental ergonomics aspects in SMEs so that strategic steps can be taken to improve these conditions.

II. METHOD

Articles included in this study were articles from scientific journals available online and can be downloaded from Google Scholar and Scopus. The search deadlines were articles which published during the past decade (2008 to 2018). In accordance with the stated objectives, article search was not limited to English-language articles, but also Indonesian-language articles. The keywords used include: “quality of workplace at Indonesia's SME” or its equivalent in Indonesian is “kualitas tempat kerja di UKM Indonesia”, “environmental ergonomics at Indonesia” (“ergonomi lingkungan di Indonesia”), “environmental comfort” (“kenyamanan lingkungan”), “thermal comfort” (“kenyamanan termal”), and “working comfort” (“kenyamanan kerja”).

In addition to taking articles from scientific journals, this study also involved several articles originating from conference proceedings which held by PEI (Perhimpunan Ergonomi Indonesia or Indonesian Ergonomics Association) in 2015 and 2017.

The articles used in this study were only articles that discuss or include environmental ergonomics parameters (temperature, lighting, noise, humidity, air quality, air velocity, and vibration) in the workplace of Indonesian SMEs in relation to work comfort, occupational safety and health, and work productivity.

As a consideration, the definition of SMEs that used in this study follows the Statistics Indonesia provisions, namely


57

Measurement of Web-Based Merchant Application Portal (MAP) Using Function Point Analysis and

Constructive Cost Model II

Irvan Santoso1,2

Harco Leslie Hendric Spits Warnars1, Benfano Soewito

Cyber Security Program, Computer Ford Lumban Gaol2, Edi Computer Science Department, BINUS

Science Department, School of Abdurachman3 Graduate Program – Master of

Computer Science1 Computer Science Program, BINUS Computer Science

Computer Science Program, BINUS Graduate Program – Doctor of Bina Nusantara University, Graduate Program – Doctor of Computer Science Jakarta, Indonesia 11480

Computer Science2 Bina Nusantara University, [email protected]

Bina Nusantara University, Jakarta, Indonesia 11480

Jakarta, Indonesia 11480 [email protected],

[email protected] [email protected], [email protected]

3

Abstract— The development of technology and the internet

is one of the critical factors that must be considered by

companies especially those engaged in e-commerce. The web-

based application is one of the tools used by e-commerce in

making it easier for users to conduct transactions and data

processing. The application that developed must be calculated

carefully so the effectiveness and efficiency can be appropriately

maintained. In this research, the calculation of the size of the

software, effort, time, staff, and the total cost needed to work on

an application was calculated using the Function Point Analysis

(FPA) and Constructive Cost Model II (CoCoMo II) methods.

The application that has been analyzed and estimated is

Merchant Application Portal (MAP) which is an application

designed by one of the companies in Indonesia. The estimation

results obtained, scilicet the size of software amounted to

10,02972 KLOC, effort amounted to 48.521 Person Month, time

development of 13 months, staff needed as many as four staff,

and the estimated cost amounted to IDR 13,828,852.79 or $

971.54. In addition, this calculation can be used by other

companies to find out the resources needed in making a software

to be more effective and efficient.

Keywords—Cost Estimation, Effort Estimation, Size Estimation, Function Point Analysis, CoCoMo II.

I. INTRODUCTION

Inevitably, technology developments have increased rapidly

which is marked by the increasing number of technologies

produced and the people who use it [1]. Technology has

penetrated all aspects of human life to support needs [2],

especially in the economic field [3]. Furthermore, the emergence

of e-commerce is used as a process of buying and selling

products or services carried out electronically by utilizing the

internet network [4]. There are various benefits obtained by e-

commerce for sellers and buyers who use it [5]. For example, the

time and cost of travel needed can be reduced; product

livelihoods become more effective and efficient; price

comparison can be performed more efficiently [6]. These benefits are one of the attractions so that e-commerce development has a significant increase [7].

As the need for e-commerce increases, it must be

accompanied by ease of use for its users [8]. Therefore, one e-

commerce company in Indonesia has designed an application for

merchants in facilitating various activities needed, namely

Merchant Application Portal (MAP). In MAP, several functions

can be used by merchants to process information

about payments, orders, billing, and even setting roles within a particular scope. However, in designing an application, it cannot only be seen from its functionality [9]. Estimation of all activities is also one of the critical factors in developing an excellent and well-targeted application [10]. Estimates and designs are usually performed to measure the effort and cost needed to determine the precise standard of software [11]. Methods that are often used in making measurements are Function Point Analysis (FPA) [12] and Constructive Cost Model II (CoCoMo II) [13].

FPA looks at all factors that have relations with software by

classifying them into several groups [14]. Moreover, each

element is given the appropriate weight to calculate the size of

software [15]. Meanwhile, CoCoMo II is used to calculate the

effort, time, staff, and cost needed [16] to make MAP which has

web-based programming. Also, the cost incurred can be adjusted

to the average labor salary in a particular area.

II. FUNCTION POINT ANALYSIS

Function Point Analysis (FPA) is a standard method in

measuring software development from the user's side [17].

FPA calculations are based on overall functions and other

factors that affect the process of running software. There are

several steps taken, including User Function Identification;

Calculating Total Value of Unadjusted Function Point;

Calculating Value adjusted Factor; Calculating Value of

Function Point and Software Size.

A. User Function Identification

In FPA, there are five User Function (UF) types as parameters used to measure software [18], as follows:

• External Input (EI): all forms of processes received from outside the software limitation.

• External Output (EO): all forms of values from

processes that resulted from application limitation.

• Internal Logical File (ILF): a group of data or information controls used in software.

• External Interface File (EIF): a group of data or

information controls that have relations with software but are managed by other software.


58

The Design of Model and Inventory Routing

Problem (IRP) Algorithm for Swapped Battery at Battery Exchange Station (BES) : Case Study of

Electric Motor

Nofan Hadi Ahmad Ahmad Rusdiansyah Alief Wikarta

Department of Industrial Engineering Department of Mechanical Engineering


Faculty of Industrial Technology Faculty of Industrial Technology


Institut Teknologi Sepuluh Nopember Institut Teknologi Sepuluh Nopember


Surabaya, Indonesia Surabaya, Indonesia

Surabaya, Indonesia

[email protected] [email protected]

[email protected]

Abstract— Electric motors are one of the achievements of

technology that is qualified, both in terms of development of

science and socio-economic aspects. Electric motors are

promising products as an alternative mode of transportation for

people by utilizing electricity as an energy source. To ensure the

reliability and sustainability of these products in order to enter

the automotive industry market and avoid negative sentiments in

the form of consumers' concerns to recharge when they run out

of electricity in the middle of their journey, it is proposed for the

establishment of Battery Exchange Station (BES) in several

locations in order to meet the availability of electrical energy

supply in the form of batteries that are ready to use. Through the

battery swapping method, electric motor users will exchange

batteries that will run out with a new battery that has been

charged 100% (fully charged). This causes the supplier is

responsible for distributing the electric battery so that a model is

needed to minimize distribution costs while maintaining

inventory levels at the customer. The inventory routing problem

model by considering stochastic demand and recharging time on

BES is designed to solve that vendor problem related to the

distribution of electric batteries. This problem is approached by

the Traveling Salesman Problem (TSP) to determine the delivery

route with a minimum total distance and modeled with the

Markov Chain to determine scheduling related to its

replenishment. The result revealed that cost of transportation

will be minimum by TSP and cost of distribution (replenishment

unit and truck dispatching) will be minimum by Modified

Markov Chain Model.

Keywords—Stochastic Demand, Recharging Time, Battery

Swapping, Battery Exchange Station, Inventory Routing Problem

I. INTRODUCTION

The case of the distribution of electric batteries for electric

motors will be necessary to be considered since there is an

increase in motorcycle users along with increasing population

and public opinion that supports the Zero Emission policy for a

better life in the future. This causes the vendor needs a model to

maintain inventory levels at each station / BES so that it can be

determined the number of batteries that will be supplied as a form

of replenishment of deficiencies that will occur in the future after

considering the battery recharge capacity. Therefore, researcher

will model the problem with an inventory routing problem (IRP)

model which considers demand to be stochastic and recharging

rates for each BES. BES is a place to exchange empty batteries

with a fully charged battery and it has a recharging system.

This conventional case of inventory routing problem (IRP)

model is common for goods that can be stored in warehouses for

longer periods of time and types of goods whose consumption

level is not too high so there is no need to reorder for a long time.

The formulation of mathematical models with Branch and Cut

for the IRP were first introduced by Archetti [1]. Archetti

explained that there are 2 types of re-fulfillment policies for

inventory levels in the Inventory model. Some are known as OU

(Order Up To Level) and ML (Maximum Level). In OU policy,

each node visited by a vehicle, its inventory level will be fully

filled so that it returns to its original condition. While in the ML

policy, each node does not have to be filled in full or in other

words that the inventory level is not always in full condition at

each fill.

Previous studies on Inventory Routing Problem (IRP) were

not too much because Vehicle Routing Problem (VRP) was more

popular with so many variants. For example, Al Khayyal and

Hwang [2], Numinarsih [3], Siswanto [4] who developed the IRP

model on the Ship Routing Problem. Even in the case of the

Electric Vehicle, VRP is more popular. For example, Adler [5],

Yang [6], Keskin [7] who developed the VRP model by

considering its location and recharge strategy.

Research of Adler [5] has aim to minimize the average delay time of all electric vehicles that will replace the batteries by making reservations in advance so that changes in routes can occur if indeed the battery power in the vehicle is still enough to reach the next station. The Markov Chain Decision Process adopted in the study is to describe the conditions on the reservation that occurs at this time and whether it needs to be changed in a route or not. Whereas in the research of Yang [6], electric vehicles that have a range of usage limits because the electric power which decreases at a certain distance and can run out in the middle of the trip, becomes the object of their research. The goal is to determine the location of the BSS (Battery Swap Station) and route planning of the fleet of electric vehicles used.

As same as the research above, Keskin [7] also discusses VRP which was developed by Time Windows and the Partial Recharging strategy for electric vehicles used in meeting a demand. In other words, the object of this research is electric vehicles that have limited range of power and must be recharged at certain stations. Therefore, the model used in this problem is VRPTW with a partial recharging strategy, namely the percentage of battery power when it has visited a particular node so that it can be known when to go to the electric energy charging station.


59

TECHNO-ECONOMICS STUDY OF SPECTRUM

SHARING FOR MOBILE NETWORK

OPERATOR IN RURAL AREA

Lia Hafiza

School of Electrical

Engineering

Telkom University

Bandung, Indonesia

[email protected]

Muhamad Reza


Engineering

Telkom University

Bandung, Indonesia

[email protected]

Nachwan Mufti

Adriansyah


Engineering

Telkom University

Bandung, Indonesia

[email protected]

Denny Setiawan

Department of Electrical

Engineering

Universitas Mercu Buana

Jakarta, Indonesia

[email protected]

g

Abstract— Telecommunication is a sector regulated by the

State because it uses limited natural resources namely

frequency and to ensure the right of everyone to be able to

communicate and obtain information in accordance with the

constitution. In the other hand, the telecommunication

industry which is predicted will decline in the future and needs

to take precautions, there are two things that become solution;

saving and entering new businesses. The solution discussed in

this study is savings. More than 60% of Mobile Network

Operators (MNO) in the world use Radio Access Network

Sharing (RAN Sharing) to make savings. The type of RAN

Sharing used in this study is the Multi-Operator Core Network

(MOCN) that shared up to the frequency spectrum, and it can

also be a solution to the scarcity of the spectrum, saving

operator’s expenses, accelerating network deployment to the

regions and impacting on GDP in Indonesia.

In this study, there are three aspects that will be discussed;

technical, economic and legal aspects. In technical aspect, rural

area use coverage dimensioning to determine the needs of

telecommunication infrastructures. In the economic aspect, the

calculation uses Net Present Value (NPV) which is analyzed

using the Game Theory approach. For the legal aspect, several

regulations in Indonesia related to spectrum sharing are

explained to see the possibility of how this sharing can be

implemented in Indonesia. Based on this research, sharing

using MOCN may providing savings in rural areas and can

affect competition between operators if only done by two

competitors. In addition, in terms of regulations, this

implementation is possible while obtaining ministerial permits

but needs further study because there is a potential change in

competition and double charge of usage rights fees (BHP).

Keywords— Network sharing, Multi-Operator Core Network,

Spectrum Frequency, Telecom Industry, Regulation of

Telecommunication.

I. INTRODUCTION

Under the Constitution of the Republic of Indonesia 1945, Article 28F states: setiap orang berhak untuk berkomunikasi dan memperoleh informasi untuk mengembangkan pribadi dan lingkungan sosial, serta berhak untuk mencari, memperoleh, memiliki, menyimpan, mengolah, dan menyampaikan informasi dengan menggunakan segala jenis saluran yang tersedia (each person has the right to communicate and obtain information to develop personal and social environment, and has the right to seek, obtain, possess, store, process, and convey information using all types of available channels).

Telecommunications is the right of every person as stated in the Constitution, so it is regulated by the State to guarantee the right to communicate and obtain information. Spectrum is important for providing wireless telecommunications and broadcasting services [1]. The radio frequency spectrum belongs to the State so it belongs to the State's public domain, the spectrum must be managed for the benefit of the national community as a whole. The main purpose of management is to maintain spectrum occupancy to be optimal and effective frequency utilization [2].

The future of telecom companies forecasted deteriorate as they have reached the peak of their revenue, this is stated in Telecom Application Developer Summit 2015. One solution for this situation is to start improving the game by devising a new strategy especially coupled with the coming era of the Internet of Things (IoT) in the form of billions of devices and exponential data growth [3], another solution is to maximize the cost. In addition to data growth, cellular subscriber growth also triggers a larger capacity requirement and requires additional investment costs. In Indonesia, the number of cellular subscribers from 2011 to 2016 incremented annually with a non-linear growth percentage [4]. ITU has released the Facts and Figures of ICT in 2017, one fact that delivered is international bandwidth increased but telecom revenue decreased. International bandwidth grew by 32% from 2015 to 2016 and global telecommunications revenues in 2014 to 2015 fell by 4%, developing countries (including Indonesia) accounted for 83% of total population but generate only 39% of total revenue [5].

Over 60% of mobile network operators in the world are done with Radio Access Network - Sharing (RAN-Sharing) to maximize cost savings. One of RAN-Sharing is Multi-Operator Core Network (MOCN) that performs sharing up to the frequency spectrum. There are some things that drive this sharing; pressure from EBITDA, the scarcity of the frequency spectrum and government policy [6].

Fig. 1. The Future of Telecommunication Companies [3]


60

Social and economic aspects when allocating a 3.5

GHz frequency band for 5G Mobile in Indonesia

Luthfijamil Setiawan Sastrawidjaja

Graduate Program of Telecommunications Management



[email protected]

Muhammad Suryanegara *

Graduate Program of Telecommunications Management



[email protected] [email protected]

*) corresponding author

Abstract – The soon-to-be allocated 5G mobile technology

using a 3.5 GHz frequency band has posed challenges

because the spectrum of 3.5 GHz has utilized satellite

services. By examining the case of Indonesia, this paper

aims to investigate the social and economic aspects of two

scenarios, i.e., implementing 5G using 3.5 GHz or retaining

the use of the satellite for 3.5 GHz. The technological

management method developed by Kano Models was used

to assess the social aspect, and the net benefit was

calculated to assess the economic aspect. The findings

indicated that most existing services offered via satellite

are in the attractive category, meaning that if such satellite

services are eliminated, the market will not change

significantly; however, implementing 5G at the frequency

of 3.5 GHz would increase economic value due to the much

higher license fee paid by the operators to the government.

Keywords— 5G, Satellite, Spectrum Management,

Indonesia, Technology Management

I. INTRODUCTION

Mobile telecommunications technology has evolved

from the first generation (1G) to the fourth generation (4G),

which has been widely adopted by an increasing number of

users around the world [1][2] [3]. This growth causes service

consequences that must be managed by the subsequent mobile

technological generation’s standards. The technical

characteristics of future services require an extremely high

data transfer speed, an extremely low delay tolerance, and a

massive connection density [1]. These standards are required

for 5G technology, which is expected to achieve gigabit data

throughput, resulting in an extremely low latency, and it will

support complex and massive communication among

machines and will increase the spectral efficiency and energy

efficiency of the system [4].

Similar to other mobile technological platforms, 5G will

operate using a certain frequency spectrum; however, many

other types of wireless technology have been allocated over a

specific frequency band, which makes the frequency a limited

resource. Thus, allocating the spectrum has become a crucial

issue that must be resolved before any country can begin

offering the 5G network infrastructure. Proper technological

management is also required for the implementation of 5G

technology to be harmonized with other existing wireless

technologies.

One prominent frequency allocation for 5G technology is

the 3.5 GHz frequency band. In Indonesia, this spectrum is

already allocated for the operation of fixed satellite services

[5]. When 5G is operated using this band, there is potential

interference between existing satellite services and the

upcoming 5G service. This has led to the concern that such a

disruption will eventually lower the quality of both services.

On the other hand, the implementation of 5G is a must because

the existing mobile platforms are not capable of supporting the

increased traffic of the Indonesian market. The number of

mobile cellular users in Indonesia has increased each year [6].

This continuously increasing number of users clearly increases

the need for the new frequency spectrum for 5G.

Based on these circumstances, it can be stated that the

implementation of 5G using the 3.5 GHz frequency

spectrum will certainly pose challenges. The aim of this study

was to investigate the feasibility of implementing 5G on the

frequency band of 3.5 GHz in the case of Indonesia. In the

authors’ previous study, the framework for analyzing the

regulatory challenges of this case was developed [7]. The

framework addresses the social, technology, economic, and

policy aspects, leading to an in-depth analysis that can be used

to develop an appropriate recommendation for Indonesian

regulators. As a part of the research, this paper focuses only on

the results of the social and economy aspects.

The presentation of this paper is started by presenting the

theories of 5G and its particular case in the country of

Indonesia (Section II). In section III, we present the research

method, focusing on framework addressing the social and

economic aspect. The two scenarios are developed, reflecting

the use of frequency band of 3.5 GHz, either allocated for 5G

technology or allocated for satellite. In Section IV, we discuss

the results of the social and economic assessments of both

scenarios. Finally, conclusions of this research is presented in

Section V.

II. UNDERLYING THEORY

A. 5G Mobile Technology

Unlike the preceding mobile generations, which have

allowed for data speed enhancement, 5G technology has three

use case scenarios: enhanced mobile broadband (eMBB),

ultra-reliable and low latency (URLLC), and massive

machine-type communications (mMTC) [1] [8]. Fig.1 shows

the role of each scenario and its technical features. eMBB is a

platform that enables 5G users to access an extremely high

data speed. An example of this service is that it only requires

2019

61

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